AI Engineer jobs in Israel

Description We are looking for a highly technical, hands-on Quality Engineering Lead to drive product quality through AI-powered automation, intelligent testing, and proactive issue detection. Your mission is to build and continuously improve automated quality mechanisms that identify bugs, regressions, performance issues, and unexpected system behavior before they impact our customers. You will leverage AI agents, modern development tools, and engineering automation to make quality an integral and continuous part of our development lifecycle. This is a senior, hands-on technical leadership role with ownership of the Quality Engineering domain across R&D. You will define the technical direction, lead cross-team initiatives, and establish the foundations for a scalable, AI-driven quality engineering function, with the opportunity to build and lead a dedicated team as the domain evolves. We are looking for someone who combines strong software engineering expertise, a passion for product quality, and the leadership skills to drive meaningful change across the engineering organization. Responsibilities • Build AI-Powered Quality Agents: Design and implement agents that proactively identify bugs, regressions, and unexpected behavior across our systems. • Develop Intelligent Testing Mechanisms: Build automated testing solutions that leverage AI to generate, execute, and maintain meaningful test scenarios. • Proactive Issue Detection: Analyze logs, metrics, traces, production behavior, and code changes to identify potential quality issues. • Automate Regression Detection: Develop mechanisms that continuously validate system behavior and detect regressions introduced by new releases. • Improve Release Confidence: Build automated quality gates and validation mechanisms integrated into CI/CD pipelines. • Production Quality Monitoring: Develop intelligent mechanisms that detect functional inconsistencies, data integrity issues, and unexpected production behavior. • AI-Assisted Root Cause Analysis: Build tools and agents that investigate detected issues, identify likely causes, and provide actionable findings to engineering teams. • Drive Quality Engineering Across R&D: Collaborate with all engineering teams to identify quality gaps, establish best practices, and drive the adoption of AI-powered quality mechanisms throughout the development lifecycle. • Technical Leadership & Ownership: Own and lead the Quality Engineering domain across R&D. Define the technical direction, drive cross-team initiatives, establish engineering standards, and mentor engineers working on quality-related solutions. • Build and Scale the Quality Engineering Function: Establish the technical foundations, processes, and tools needed to scale quality engineering across the organization, with the potential to build and lead a dedicated team. • Own Quality Improvements End-to-End: Independently design, develop, deploy, and maintain quality engineering tools and automation, ensuring measurable improvements in product quality. Requirements • 7+ years of hands-on software engineering, quality engineering, or test infrastructure development experience. • Proven technical leadership experience as a Tech Lead, Staff Engineer, or equivalent, with the ability to drive cross-team initiatives and influence engineering practices. • Strong programming skills in Python, TypeScript, or similar languages. • Extensive experience building automated testing frameworks, quality validation systems, or engineering automation infrastructure. • Hands-on experience using LLMs, AI coding tools, or AI agents to solve engineering problems. • Strong understanding of backend systems, APIs, distributed architectures, and cloud environments. • Experience with CI/CD pipelines, integration testing, and automated regression testing. • Strong debugging, troubleshooting, and root cause analysis skills. • Ability to define technical direction, mentor engineers, and take ownership of an engineering domain across the organization. • Strong communication and collaboration skills, with the ability to work effectively across multiple engineering teams. • Ability to independently identify quality gaps and deliver production-ready engineering solutions. Nice to Have • Experience building autonomous AI agents or agent-based testing frameworks. • Experience with Claude Code, Cursor, Codex, or similar tools. • Experience with Kubernetes, AWS, and microservices architectures. • Experience with observability platforms such as Datadog, Groundcover, or Grafana. • Experience with performance testing, chaos engineering, or production validation. • Familiarity with MCP, LLM tool calling, and agent orchestration. • Experience establishing or leading quality engineering initiatives across multiple development teams. • Experience building or scaling engineering teams or technical functions. What Success Looks Like • Critical bugs and regressions are detected before reaching customers. • Automated quality mechanisms continuously validate product behavior across development and production environments. • AI agents reduce manual testing, debugging, and investigation efforts. • Engineering teams receive actionable quality findings early in the development cycle. • Release confidence increases while maintaining or improving development velocity. • Product quality improves through measurable reductions in escaped defects and recurring incidents. • Quality engineering becomes a shared, AI-powered capability adopted across all R&D teams. • A scalable quality engineering function is established, with clear technical ownership, standards, and long-term direction. Our Vision Build an engineering organization where quality is continuously and proactively validated through intelligent automation and AI agents. • We believe quality should not depend solely on manual testing or reactive bug fixing. Our goal is to create an AI-driven quality engineering function that continuously identifies problems, prevents regressions, and empowers every engineering team to deliver reliable software faster.

Description Gloat is the AI-native platform powering the era of Agentic HR. Powered by Loomra, our context engine is purpose-built for the workforce. Gloat lets you build any Agentic HR agent that operates in the flow of work across Slack, Microsoft Teams, Copilot, and Google Chat. Talent Redeployment, internal mobility, skills intelligence, workforce redesign, and whatever comes next. Trusted by leading global enterprises, Gloat serves over 1.5 million employees across 30+ Fortune 500 companies, unlocking more than 4.8 million strategic work hours. We help organizations harness AI to unlock agility, productivity, and growth, empowering them to achieve true ROI and drive exponential productivity. About the Role We are looking for a highly driven and experienced AI Engineer Team Leader to manage and mentor a core team of engineers while remaining 50% hands-on. In this critical role, you will lead the development of our enterprise Agentic AI platform, blending deep technical expertise in AI agent development with strong engineering leadership. As a Team Leader, you will drive the technical agenda, plan sprints, and collaborate closely with Product Managers and cross-functional partners to turn powerful capabilities into reliable, safe, and fast product experiences. You must thrive in a high-pressure, dynamic startup environment characterized by a fast pace and frequent pivots, ensuring your team remains focused, agile, and aligned with business goals while executing at the highest level. You won't just manage - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily, and iteration in front of enterprise customers. Responsibilities • Team Leadership & Management (50%): Manage, mentor, and grow a team of AI and software engineers. Oversee task allocation, sprint planning, and daily agile ceremonies. • Hands-On Engineering (50%): Actively code, design, and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents. • Technical Strategy & Architecture: Drive the technical agenda, architecture decisions, and best practices for developing scalable, LLM-driven applications grounded in our knowledge graph. • System Integration: Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows. • Production Excellence: Build evaluation harnesses, guardrails, and safety/bias checks. Optimize agents for latency, cost, and reliability at enterprise scale. Take agents from prototype to production - with monitoring, observability, and a fast iteration loop. • Cross-Functional Collaboration: Work intimately with Product Management, Data, and Design teams to translate customer needs into agent capabilities, define roadmaps, and prioritize tasks. • Agility & Execution: Lead the team through frequent pivots and rapid development cycles, ensuring high-quality delivery under pressure in a fast-paced startup environment. Requirements Must-Have: • 2+ years of experience managing or leading a high-performing software/AI engineering team. • 5+ years of hands-on experience as a Backend or AI/Software Engineer building complex, scalable distributed systems in production. • Proven hands-on experience building and shipping LLM agents to production - not just demos or prototypes. • Hands-on with at least one agent orchestration framework (e.g. LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar). • Strong Python and solid software engineering fundamentals. • Strong agile project management skills with a track record of effective sprint planning and execution. • Demonstrated ability to thrive in a dynamic, high-pressure startup environment with rapid execution and frequent shifts in direction. • Excellent communication skills, with the ability to build strong partnerships with Product Managers and other stakeholders. Nice-to-Have: • 2+ years hands-on with LLMs / generative AI. • Practical experience with RAG, embeddings, and vector databases (e.g. pgvector, Pinecone, or similar). • Experience with tool use / function calling and integrating LLMs with external systems. • Experience with MCP, agent memory, and planning/reasoning patterns. • Responsible AI: bias evaluation, guardrails, and AI governance.

Description Gloat is the AI-native system of action for workforce orchestration. Our agentic HR platform lets enterprises build intelligent workforce agents that understand their people, respect their policies, and work where teams already are — Microsoft Teams, Slack, Google Chat, and Copilot, with no new portal or change management. The platform is powered by Loomra, our semantic layer that connects people, jobs, and skills through a knowledge graph, deep HCM integration (Workday, SuccessFactors, Oracle HCM), a governance engine, and semantic retrieval — with a full audit trail behind every agent decision. Trusted by 100+ enterprises across 112 countries, empowering 5M+ employees. This role sits at the center of that effort. We're looking for a Senior AI Engineer to design, build, and ship production-grade LLM agents that reason over workforce and skills data on top of Loomra's semantic layer. You won't just prototype — you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily (Teams, Slack, Copilot), and iteration in front of enterprise customers. You'll work alongside product, data, and platform engineers to turn powerful capabilities into reliable, safe, and fast product experiences. If you've built agents that actually made it to production — and you care as much about evaluation, guardrails, and reliability as you do about capability — we want to talk to you. Responsibilities • Design and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents. • Implement retrieval and RAG pipelines over structured and unstructured workforce data, grounded in our knowledge graph connecting people, jobs, and skills. • Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows. • Build evaluation harnesses, guardrails, and safety/bias checks, and work within the governance engine so agents behave reliably, respect customer policies, and produce a full audit trail. • Ship agents in a model-agnostic way across providers (Anthropic, Google, IBM watsonx) and deploy them into Teams, Slack, and Copilot. • Optimize agents for latency, cost, and reliability at enterprise scale. • Take agents from prototype to production — with monitoring, observability, and a fast iteration loop. • Partner closely with product, data, and platform teams to translate customer needs into agent capabilities. Requirements • 5+ years building production software • Proven experience building and shipping LLM agents to production — not just demos or prototypes. • Hands-on with at least one agent orchestration framework (e.g. LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar). • Strong Python and solid software engineering fundamentals. • Prompt engineering paired with systematic, measurable evaluation of LLM outputs. • Experience with tool use / function calling and integrating LLMs with external systems. • Track record deploying, monitoring, and maintaining AI in production (cloud, CI/CD, observability). Nice to Have • 2+ years hands-on with LLMs / generative AI. • Practical experience with RAG, embeddings, and vector databases (e.g. pgvector, Pinecone, or similar) • Experience with MCP, agent memory, and planning/reasoning patterns. • Background in HR tech, people data, or skills ontologies. • Knowledge graph / graph ML experience (knowledge graphs, GNNs). • Responsible AI: bias evaluation, guardrails, and AI governance. • Experience working across multiple model providers (e.g. Anthropic, Google, IBM watsonx) rather than a single vendor.

Description Sygnia is a top tier cyber technology and services company, providing high-end consulting and incident response support for organizations worldwide. Sygnia works with companies to proactively build their cyber resilience and to respond and defeat attacks within their networks. It is the trusted advisor and cyber security service provider of IT and security teams, senior managements, and boards of top organizations worldwide, including Fortune 100 companies. The company draws on top talent from the ranks of elite military technology units and from across the cyber industry and has some of the world’s top talents in cybersecurity. Described by Forbes as a “cyber security delta force”, it applies technological supremacy, digital combat experience, data analytics and a business-driven mindset to cyber security, enabling organizations to excel in the age of cyber. Our Technology division builds a world class product, considered by experts as the leading XDR platform for cyber analytics and investigations. Our product was deployed in hundreds of clients, including many Fortune 500 companies, all around the world (from North America through Europe, APAC and all the way to Australia), and as a part of the team you will have the privilege to take part in the ongoing fight against cyber threat actors of the highest levels. We are looking for a talented, passionate, and experienced Senior GenAI Engineer to join our team. You will design and build production-grade GenAI applications from the ground up, using technologies such as Google ADK, Docker, and AWS (Bedrock, AgentCore etc). We’re looking for someone with a genuine passion for GenAI, strong software engineering skills, hands-on experience developing GenAI solutions, and a collaborative, hands-on approach. You will also work closely with the team’s researchers, helping transform research code and prototypes into reliable, secure, and scalable containerized applications that can be deployed and operated in the cloud. Main Responsibilities: • Design and development of production-grade GenAI applications and agentic systems • Build and operate reliable, secure, and scalable AI-powered services, with a focus on performance, observability, and cost • Drive engineering best practices, including testing, evaluation, CI/CD, code review, and technical design, while contributing to product and technical innovation • Work closely with the team’s researchers to transform research code and prototypes into reliable, containerized applications that can be deployed and operated in the cloud Requirements Main Requirements: • 5+ years of software engineering experience building production systems • 3+ years of professional Python development experience • Hands-on experience designing and developing production GenAI applications • Strong understanding of LLMs, agentic systems, RAG, tool calling, prompt engineering, and evaluation • Experience using GenAI observability tools for tracing, monitoring, evaluating, optimizing and debugging AI applications • Strong experience with Docker and cloud environments (preferably AWS). • Strong coding standards and experience with testing, CI/CD, observability, and production troubleshooting • Strong communication skills and the ability to clearly articulate technical ideas and trade-offs • High proficiency in English, both written and spoken • A collaborative, responsible team player with a genuine passion for GenAI and continuous learning Advantage: • Hands-on experience with Google ADK • Experience defining AWS infrastructure using AWS CDK • Experience with AWS GenAI services such as Amazon Bedrock and Amazon Bedrock AgentCore • Experience developing AI solutions for cybersecurity or security products

Location: Kfar Saba, IL (hybrid ERGO NEXT's mission is to help entrepreneurs thrive. We’re doing that by building the only technology-led, full-stack provider of small business insurance in the industry, taking on the entire value chain and transforming the customer experience. Simply put, wherever you find small businesses, you’ll find ERGO NEXT. Since 2016, we’ve helped hundreds of thousands of small business customers across the United States get fast, customized and affordable coverage. We’re backed by industry leaders in insurance and tech, and we still have room to grow — that’s where you come in. We are seeking a product-minded AI Engineer to design, build, and ship end-to-end AI-driven capabilities to our system. The role focuses on rapid execution, pragmatic product and business awareness, and full-stack ownership in a fast-paced, highly dynamic environment. What You’ll Do: • Build AI-driven applications and features end-to-end, including any task necessary (UI, light devOps, data cleanups, etc.) required to achieve successful delivery. • Work with internal stakeholders and product teams to translate raw, shifting requirements into clear technical solutions. • Leverage 100% Agentic Coding workflows to maintain exceptional development velocity and capability. • Diagnose and resolve issues across the entire feature stack with a strong bias for action. • Apply critical thinking to separate essential business value from noise, actively identifying risks and challenging requirements when appropriate. What We Need: • 5+ years of meaningful software engineering experience. • Proven experience of delivering software to customers and a willingness to work on any part of the tech stack. • Daily proficiency with Agentic Coding approach (e.g., Cursor, Claude, Codex, etc.). • High comfort level with ambiguity, dynamic environments, and self-directed execution. • Strong communication skills and a pragmatic, product-first approach to problem-solving. • Hybrid Work Model: This role supports a flexible hybrid work model, requiring a minimum of three days per week on-site in the office, per company policy. Advantage • Hands-on experience developing LLM applications (most notable: Evaluations and RAG). • Background as an early-stage startup employee, founder, or active side-project builder. Note on Fraudulent Recruiting We have become aware that there may be fraudulent recruiting attempts being made by people posing as representatives of ERGO NEXT Insurance. These scams may involve fake job postings, unsolicited emails, or messages claiming to be from our recruiters or hiring managers. Please note, we do not ask for sensitive information via chat, text, or social media, and any email communications will come from the domain @next-insurance.com or @nextinsurance.com. Additionally, Next Insurance will never ask for payment, fees, or purchases to be made by a job applicant. All applicants are encouraged to apply directly to our open jobs via the careers page on our website. Interviews are generally conducted via Zoom video conference unless the candidate requests other accommodations. If you believe that you have been the target of an interview/offer scam by someone posing as a representative of Next Insurance, please do not provide any personal or financial information. You can find additional information about this type of scam and report any fraudulent employment offers via the Federal Trade Commission's website (https://consumer.ftc.gov/articles/job-scams), or you can contact your local law enforcement agency. Don’t meet every single requirement? Studies have shown that some underrepresented people are less likely to apply to jobs unless they meet every single qualification. At ERGO NEXT, we are dedicated to building a diverse, inclusive and respectful workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. One of our core values is 'Play as a Team'; this means making sure everyone has an equal chance to participate and make a difference. We win by playing together. ERGO Next Insurance is an equal opportunity employer and prioritizes building a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants of any type and do not discriminate based on race, color, religion, national origin, gender, age, sexual orientation, physical or mental disability, genetic information or characteristic, gender identity and expression, veteran status, or other non-job-related characteristics or other prohibited grounds specified in applicable federal, state, and local laws. ERGO Next's policy is to comply with all applicable laws related to nondiscrimination and equal opportunity and will not tolerate discrimination or harassment based on any of these characteristics. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Description At Classiq Technologies, we’re shaping the future of quantum computing. Our mission is to make quantum computing practical and impactful by bridging the gap between hardware and real-world applications. Our platform enables developers and enterprises to design, optimize, and deploy quantum algorithms at scale, automating complex processes and supporting teams from beginners to experts across diverse architectures. Classiq is trusted by Fortune 500 leaders and global governments. We accelerate the quantum era with a platform that transforms bold quantum ideas into working applications. We're looking for someone to make Classiq AI-native. You'll build agentic systems that do real SDLC work, find the bottlenecks in our processes and build the AI tooling that removes them. Agentic pipelines, internal tools, whatever the work demands. High ownership and a direct say in what gets built next. What you'll do • Own the SDLC pipeline: ticket sync → planning → implementation → PR • Design agentic systems: prompts, tools, evals, guardrails, human-in-the-loop approvals, cost/latency tradeoffs • Build integrations across Jira, GitHub, Figma, and LLM providers • Own the roadmap, rollout, and adoption of internal AI tooling Who you are • 5+ years of relevant professional experience • BSc degree in Computer Science, Software Engineering, Physics, Mathematics, or a related field. • Strong TypeScript/Node and/or Python • Production LLM/agent experience: tool calling, orchestration, context pipelines, evals • Ability to own a workstream from requirements through iteration and drive adoption without a team under you Bonus points • durable workflows (Restate/Temporal), React internal tools, agent tooling (Claude Code, Cursor, MCP), Kubernetes/Datadog, interest in quantum. Stack • TypeScript/Python, Restate, PostgreSQL, Anthropic/Bedrock/LiteLLM, Jira & GitHub APIs, AWS, Terraform. Requirements Who you are • 5+ years of relevant professional experience • BSc degree in Computer Science, Software Engineering, Physics, Mathematics, or a related field. • Strong TypeScript/Node and/or Python • Production LLM/agent experience: tool calling, orchestration, context pipelines, evals • Ability to own a workstream from requirements through iteration and drive adoption without a team under you Bonus points • durable workflows (Restate/Temporal), React internal tools, agent tooling (Claude Code, Cursor, MCP), Kubernetes/Datadog, interest in quantum. Stack • TypeScript/Python, Restate, PostgreSQL, Anthropic/Bedrock/LiteLLM, Jira & GitHub APIs, AWS, Terraform.

Description About Us We are an NVIDIA partner, delivering professional services in advanced software development, artificial intelligence systems, and high-performance AI solutions. We build production-grade AI systems optimized for NVIDIA GPU platforms, working across multiple applied AI domains, including Generative AI / Agentic Systems. Role Overview We are looking for an experienced AI Software Engineer to take a leading role in designing, building, and delivering advanced AI systems for real-world applications. This role is suited for a hands-on engineer with strong software engineering skills and deep experience in applied AI, who enjoys solving complex problems, owning technical solutions end-to-end, and mentoring junior team members. Projects typically focus on LLM or Multimodal LLM based agentic systems. What We Work On Our work spans software engineering and AI domains: • Design and implementation of agentic systems powered by Large Language Models (LLMs) • Deployment of open-source LLMs on NVIDIA GPUs • High-performance inference using NVIDIA technologies such as NVIDIA NIM (Inference Microservices) • System-level design for reliability, scalability, and performance • Multimodal agents for text, image, audio, and video processing • Performance optimization on GPU platforms Key Responsibilities • Lead the design and development of LLM-based agentic systems. • Own end-to-end delivery of AI solutions, from architecture and prototyping to production deployment using rigorous software engineering practices • Optimize model performance, latency, and throughput on NVIDIA GPU platforms • Design clean, maintainable, and scalable software architectures • Collaborate with customers, product teams, and engineers to translate requirements into technical solutions • Mentor junior engineers and contribute to technical best practices • Evaluate new tools, AI models, and frameworks and drive their adoption when appropriate Requirements • BSc or MSc in Computer Science, Electrical Engineering, or a related field • 4+ years of experience in software engineering and applied AI (or equivalent) • Strong knowledge of software design and development practices • Strong proficiency in Python and modern AI frameworks • Proven experience delivering production-grade AI systems • Solid understanding of deep learning architectures (CNNs, transformers) • Experience with system-level design, debugging, and performance optimization • Experience working with Large Language Models (LLMs) • Building agentic workflows, reasoning systems, or AI-driven applications • Deploying and optimizing open-source LLMs for inference Nice to Have • Experience with NVIDIA technologies (CUDA, TensorRT, Triton, NVIDIA NIM) • Experience with GPU performance profiling and optimization • Background in high-performance or low-latency systems • Experience with Multimodal LLM agents for text, image, audio, and video processing • Experience with React, TypeScript, Redis Pub/Sub, SQL, Containerized Development • Experience with Test-Driven Development, Agile Scrum methodology, Git workflows • Experience mentoring engineers or leading technical initiatives What We Offer • Ownership of complex, high-impact AI projects • Work with cutting-edge NVIDIA GPU and AI technologies • Influence over architecture, tooling, and technical direction • A collaborative, engineering-driven culture • Opportunities for technical leadership and professional growth • Real-world, production-scale AI challenges Full time Job Location: Haifa, Hybrid We at Deloitte believe that diversity and inclusion among our people is a critical component of our success and that is why we cultivate an organizational culture that contains and embraces diversity in all its forms.

Description #Hybrid DriveNets builds high-performance networking solutions for large-scale AI infrastructure and service-provider networks. DriveNets’ Network Cloud powers production networks at global operators such as AT&T and Comcast, while its AI Fabric solutions optimize the performance and time to first token (TTFT) in multi-vendor AI environments at foundation model labs, NeoClouds, and enterprises. DriveNets combines high-performance networking, end-to-end automation, and full-stack AI cluster optimization to remove network bottlenecks, maximize GPU utilization, accelerate AI cluster deployments, and improve token economics Job Summary We are looking for a talented and motivated Software Engineer with hands-on experience building and operating multi-agent AI systems in production to join our DriveNets Automation Platform (DAP) team. The team develops automation tools, orchestration capabilities, and intelligent platforms that simplify the deployment, management, troubleshooting, and optimization of large-scale network and AI infrastructure environments. You will work on the design and development of AI-powered systems that bridge networking, automation, observability, and distributed infrastructure — running on Kubernetes at scale. Our stack includes LangGraph, Langfuse, RAG pipelines, MCP, and agent-to-agent (A2A) communication patterns. This role combines strong software engineering with practical AI application development, with a sharp focus on production hardening, tracing, evaluation, and safety of agentic systems — not model training or research prototypes. Requirements • 8+ years of hands-on software engineering experience building production-grade backend services, APIs, or AI-powered systems. • Proven production experience with multi-agent AI systems: deployment, tracing, guardrails, hardening, and incident management. • Hands-on experience with agentic frameworks such as LangGraph, CrewAI, Google ADK, AutoGen, or equivalent. • Experience building and running evaluation pipelines for agentic solutions - including trajectory tracing, ground truth validation, and harshness/quality scoring. • Strong Python proficiency: comfortable building scalable backend services using gRPC and REST APIs. • Solid understanding of distributed systems: fault tolerance, consistency models, service communication, and operational challenges at scale. • Hands-on Kubernetes experience: deploying and operating containerized services, managing workloads, config, and scaling in production clusters. • Practical experience with embeddings, vector databases, and semantic retrieval systems in production. • Practical experience with RAG pipelines, LLM API integration, structured outputs, and tool calling in production environments including building and serving MCP servers at scale. • Working knowledge of SQL and/or NoSQL databases, schema design, and query optimization. • Strong debugging skills across application logic, APIs, data, and AI agent behavior. • Strong communication skills and a bias toward ownership and delivery. Nice to Have • Familiarity with Langfuse/Arize Pheonix. • Familarity with A2A & A2UI protocols. • Experience with network automation, orchestration, or configuration management (Ansible, Terraform, NETCONF, gNMI, or similar). DriveNets is on a mission to build a special company comprised of individuals with diverse backgrounds, perspectives, and experiences. DriveNets is an equal opportunity employer. We do not discriminate based on upon race, religion, national origin, sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with disability, or other applicable legally protected characteristics.

Description A winning ad could change the trajectory of a mobile game. The only way to consistently find winners is to explore more, faster, and smarter than anyone else. Sett is a UA creative performance platform for mobile gaming. We build agentic AI systems that analyze performance data, generate diverse playable and video ad concepts, deploy them to ad networks, and continuously learn from the results. The full creative loop. Automated, and done better than humans only. Our system doesn't remix templates. It creates fundamentally different creative hypotheses, each one built from real performance signals. Sett is looking for a Senior AI Software Engineer to join our out-of-this-world, cross-functional team. If you’re someone who loves taking ownership, enjoys working across the stack, and isn’t afraid to dive into new technologies - this might be your perfect role. Responsibilities • Own end-to-end feature development - from design and coding to testing and production • Work on LLM based applications including the development of proprietary AI agents • Collaborate with team members through design/code reviews and open discussions • Partner closely with research and engineering to deliver impactful solutions Requirements • At least 8 years of experience in software development - a MUST • Strong hands-on experience with Python (at least 4 years) • Minimum 2 years of experience as an ML Engineer, Data Scientist, or in a similar AI-focused role - a MUST • Proven ability to develop and maintain complex backend systems • Curiosity, drive, and a passion for building great tech

Description A winning ad could change the trajectory of a mobile game. The only way to consistently find winners is to explore more, faster, and smarter than anyone else. Sett is a UA creative performance platform for mobile gaming. We build agentic AI systems that analyze performance data, generate diverse playable and video ad concepts, deploy them to ad networks, and continuously learn from the results. The full creative loop. Automated, and done better than humans only. Our system doesn't remix templates. It creates fundamentally different creative hypotheses, each one built from real performance signals. You'll build products where AI isn't a feature - it's the product. From customer-facing experiences to the multi-agent systems behind them, you'll own problems end-to-end, ship continuously, and help define how people work with AI. Responsibilities • Build customer-facing AI products from idea to production. • Design and build AI agents that solve real customer problems. • Create and improve agentic workflows using LangGraph, LangChain, and the Claude Agent SDK. • Build the orchestration layer that lets multiple AI agents collaborate, reason and complete complex tasks. • Own features end-to-end, from React UI to Python backend. • Design APIs and distributed services powering our agentic platform. • Improve reliability, observability and performance across production AI systems. • Work closely with Product, Design and AI Engineers to ship continuously. Requirements • You have 6+ years of experience building web products with Python, React, and TypeScript. • You know how to design clean APIs and scalable architectures. • You have experience with AWS, Docker, Kubernetes, and microservices. • You care about reliability, observability, and production quality (logs, metrics, tracing, and monitoring). • You have built or worked on production AI applications using tools like LangGraph, LangChain, the Claude Agent SDK, or similar frameworks. • You like shipping fast, owning problems end-to-end, and working across the entire stack. • You enjoy solving ambiguous problems and turning ideas into production software

About Aidoc Aidoc is the market leader in healthcare AI and has developed the world's largest clinical frontier model, supporting nearly 50 million patients each year. Aidoc is deployed in more than 2,000 medical centers worldwide. Since our founding in 2016, Aidoc has raised over $500 million and achieved a record number of FDA-cleared solutions. Medical diagnosis is hitting a breaking point, where the number of physicians hasn't kept up with the rapidly growing patient load. This leads to misdiagnosis and treatment delays that result in hundreds of thousands of deaths every year. Aidoc assists clinicians by precisely highlighting diseases directly on medical images, preventing misdiagnosis and ensuring that urgent patients receive the immediate attention they need. About this role This is a AI Software Tech Lead, ML role within Aidoc’s AI Engineering group, focused on building and scaling the infrastructure behind advanced AI solutions in computer vision and NLP for medical imaging. The role combines hands-on software development, technical leadership, and close collaboration with cross-functional teams to transform AI innovation into scalable, high-impact products. Responsibilities • Design, develop and lead SW development of infrastructure for innovative AI algorithms for computer vision and NLP in the context of medical imaging. • Collaborate with cross-functional teams, including researchers and product managers, to drive the development of new AI products and features. • Work in a unique professional culture applying the full range of software engineering capabilities in the core parts of the A.I. systems • Develop and maintain high-quality, scalable, and efficient code. • Take initiative in exploring and adopting of new technological advances in all relevant domains (for example, scalable cloud based systems, DL and CV SW frameworks, Large scale ML systems) Requirements • BA/BSc/MSc in Computer Science, Engineering or a related field. • 7+ years of experience in developing large scale production systems – including architecture design, research, development and testing. • Deep understanding of Computer Science fundamentals – object-oriented design, code optimization, and complexity • Passion for building generic and robust software architecture • Passion for elegant and effective technological solutions, and a love for clean code • Strong experience with Python and advantage to deep learning frameworks such as TensorFlow, PyTorch, etc. • Strong ability to lead end-to-end development processes • Advantage: Experience in developing AI and machine learning solutions, with a focus on computer vision and NLP. • Strong communication and collaboration skills, with the ability to work effectively with a cross-functional team Working at Aidoc Our Perks: • Be part of something big - using cutting-edge technologies to transform the Healthcare industry (while saving patients’ lives) • We work in a hybrid model, with our new offices located at 34 HaMasger Street in Tel Aviv and parking for employees. • Amazing and healthy breakfasts and lunches prepared daily by our personal chef! • Stocked up kitchen & meal card • Wellness: Aidoc employees-only gym, plus Pilates, Yoga, and functional workouts classes. • Amazing culture - collaborative, transparent & fun! • Attractive compensation package & benefits Aidoc is deeply committed to creating an inclusive workplace, and to the principle of equal opportunity for all individuals. We prohibit discrimination and harassment based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by law.

Description We're building an agentic AI platform that helps enterprises transform complex business workflows into AI-powered operations. We work with leading global companies to identify high-value workflows, deploy AI agents into real business environments, and help organizations move from experimentation to measurable enterprise impact. We believe AI adoption inside the enterprise isn't just a technology challenge. It requires business context, workflow understanding, change management, trust, ownership, and a clear path to value. That's where this role comes in. We are a well-funded, early-stage startup, looking for a talented and motivated AI Engineer to join our team. The focus of this role is to develop advanced AI agents aimed at automating and optimizing low-skill human tasks, with a special emphasis on document processing, data extraction, text and email comprehension, and related workflows. You will be responsible for researching, designing, and deploying AI solutions that can replace repetitive manual tasks, improving efficiency and scalability for organizations. Your Impact AI Agent Development • Design, develop, and implement AI agents using best of breed LLMs to automate document processing tasks across enterprise workflows. Workflow Automation • Build machine learning pipelines to automate content-heavy processes for enterprise businesses. Model Optimization • Fine-tune and optimize pre-trained LLMs to accurately interpret, classify, and process large volumes of documents, improving speed and accuracy. Collaboration • Partner with product, engineering, and business teams to identify high-value automation opportunities and integrate AI-driven solutions into our operations. Evaluation & Testing • Develop metrics and conduct extensive testing to ensure reliability and efficiency of the AI systems in real-world scenarios. Requirements What It Takes Experience • 5+ years of experience as a software engineer or data scientist building production systems. • 2+ years of experience with large language models and NLP techniques. • Background in developing AI agents for content heavy tasks. • Experience with multiple LLM Vendors, RAG or Fine Tuning OSS or Commercial models. Technical Skills • Proficiency in Python and machine learning libraries such as TensorFlow or PyTorch. • Deep understanding of LLM architectures and NLP applications like entity recognition, document classification, and summarization. • Experience with model grounding techniques, retrieval-augmented generation (RAG), and deploying robust AI models in production environments. • Skills in designing and deploying AI models through APIs, containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP, Azure). • Understanding of best practices for continuous monitoring, evaluation, and iterative improvement of deployed models.

Description Upwind is a next-generation Cloud Security Platform that uses runtime context to identify and prioritize critical risks. Unlike traditional tools, Upwind uses runtime data to prioritize risk and inform posture, so teams can focus on what truly matters. Our eBPF-powered sensors and agentless cloud discovery deliver real-time threat protection, posture management, and API security. Together they protect cloud infrastructure end to end, from misconfigurations to malware. At Upwind, you'll have room to think creatively, explore new ideas, and make a real impact on our growth. We're looking for a hands-on AI Engineer to join Upwind's Research team. You'll help build our core intelligence engine: LLM agents that run cloud security workflows end to end. These agents investigate runtime detections, reason over workload, identity, and cloud context, and drive remediation in production. You'll research new models and agent designs, run rigorous experiments, and ship what works as high-impact features in our platform. What you will do • Build LLM-based agents that investigate, triage, and remediate cloud security threats. • Own the path from research to production: prototype new models, techniques, and agent designs, and ship the ones that work. • Train, fine-tune, and evaluate models for security tasks using Upwind's vast data sources: runtime events, cloud logs, identity, and network activity. • Build and extend our internal AI platform: services, pipelines, evals, and observability. • Collaborate with Security Researchers, Product, and Engineering to turn research findings into production-grade autonomous workflows. Requirements • 3+ years building AI/ML systems in production, including LLM-based agents built with modern AI and agent frameworks. • Strong backend engineering skills and proficiency in Python. • Experience training or fine-tuning ML models and LLMs. • Strong understanding of AI system reliability: prompting and context engineering, evaluation pipelines, LLM-as-judge, regression testing, and production monitoring. • Proven AI research ability: track the literature, frame open problems, design rigorous experiments, and benchmark new techniques against the state of the art. • Strong ownership and the ability to work both independently and as part of a team in a fast-paced environment, bridging research and product engineering. • Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Nice to have • Background in cybersecurity (e.g., cloud security, threat detection, incident response). • Hands-on experience with cloud platforms (AWS, GCP, Azure) and Kubernetes. • • Research publications, open-source contributions, or other work that shows depth in AI.

Description Upwind is a next-generation Cloud Security Platform that leverages runtime context to identify and prioritize critical risks, providing precise insights and efficient cloud security management. Unlike traditional tools, Upwind uses runtime data proactively for risk prioritization and posture insights, ensuring teams focus on what truly matters. With industry-leading efficiency and eBPF-powered sensors, Upwind delivers comprehensive capabilities, including agentless cloud posture discovery, real-time threat protection, and integrated API security. From misconfigurations to malware defense, Upwind ensures end-to-end, cost-effective cloud infrastructure protection. At Upwind, you'll have the opportunity to work on some of the most challenging problems in applied AI and security, turning cutting-edge research and LLM capabilities into products that operate in real customer environments. We're looking for an AI Engineering Lead to help drive the quality, intelligence, and continuous improvement of Upwind's AI and agentic systems. This is a hands-on AI engineering lead role focused on data, evaluation, and agentic workflows. You'll build the systems and methodologies that allow us to understand how our agents perform, identify where they fail, and continuously improve their reasoning, tool usage, and outcomes. You'll work closely with AI Researchers, Engineers, and Product teams to turn experiments into reliable, high-performing AI systems — from defining evaluation datasets and metrics to improving agentic workflows and deploying them at scale. What You’ll Build: • Agentic Workflows: Design and continuously improve multi-step agent workflows, including reasoning, tool usage, planning, state management, and recovery strategies. • AI Evaluation: Build evaluation frameworks, datasets, metrics, and automated pipelines to measure the quality and reliability of LLMs and agents. • AI Data Infrastructure: Build the data pipelines and feedback loops required to collect, curate, analyze, and learn from real-world agent interactions • AI Observability: Build visibility into agent behavior and performance, including trajectories, tool calls, latency, failures, and outcome quality. Responsibilities: • Own and evolve the evaluation strategy for Upwind's AI agents and LLM-powered features. • Define meaningful metrics and automated evaluation pipelines for measuring agent quality, accuracy, reliability, and task completion. • Design and improve agentic workflows to make agents more capable, reliable, efficient, and predictable. • Build feedback loops from production usage back into evaluation datasets and development workflows. • Develop tooling for running experiments and comparing models, prompts, workflows, and agent configurations. • Work closely with AI Researchers to turn research ideas and experiments into production-quality AI capabilities. • Collaborate with backend engineers to integrate AI workflows and evaluation systems into our production platform. • Establish best practices for testing and validating non-deterministic AI systems. • Monitor AI systems in production and continuously identify opportunities for improvement. • Take end-to-end ownership of projects- from defining the problem and building the first experiment to productionizing and measuring its impact. Requirements • Strong engineering experience, with a track record of owning and delivering complex technical systems. • Hands-on experience building LLM-powered applications, agents, or AI systems. • Strong understanding of LLM evaluation, experimentation, and data-driven development. • Experience designing and implementing LLM-as-a-Judge evaluations, including evaluation criteria, scoring rubrics, structured outputs, and validation of judge reliability. • Strong problem-solving skills and the ability to analyze complex AI behavior and turn observations into concrete improvements. • Experience working with agentic workflows, tool calling, multi-step reasoning, or AI orchestration. • Ability to work closely with researchers and engineers and bridge the gap between experimentation and production. • Comfortable working in a fast-paced environment where requirements and technologies evolve quickly. Strong Advantages: • Experience with LLM evaluation frameworks and automated evaluation pipelines. • Proven experience with LLM-as-a-Judge methodologies, including calibrating judges against human evaluations, measuring evaluator consistency, and reducing evaluation bias • Experience with LangGraph, LangChain, or comparable agent orchestration frameworks. • Experience working with security, infrastructure, or other complex technical domains. • Experience building production AI systems where correctness and reliability are critical. • Strong Python skills and experience working with data processing and AI/ML tooling. Why Upwind? • You'll have the opportunity to work on AI systems that are solving real, complex security problems- not demos or prototypes. • You'll be part of a team building the next generation of AI-powered security agents, with the opportunity to influence how we evaluate, improve, and scale these systems from the ground up.

Job Description • Agentic Framework Design: Design and implement intelligent, multi-agent AI systems capable of executing complex security analysis workflows and automated decision-making. • Context Optimization: Analyze diverse security datasets to determine the most effective context, telemetry data, and threat intelligence to feed into AI agents to maximize decision accuracy and minimize false positives. • Model Configuration & Integration: Evaluate, configure, and deploy a variety of state-of-the-art language models and AI architectures tailored for high-performance security use cases. • Data Analysis & Performance Evaluation: Perform deep data analysis on security events and model outputs to continuously refine agent logic, benchmark performance, and optimize AI-driven detection mechanisms. • Collaboration & Deployment: Partner with security research and engineering teams to integrate intelligent AI components into production systems seamlessly and securely. Qualifications Required Technical Skills • AI & Agentic Systems: Proven experience building applications with modern AI architectures, advanced prompt engineering, and multi-agent orchestration frameworks. • Model Management: Hands-on experience configuring, optimizing, and deploying diverse machine learning and language models. • Data Processing & Analysis: Strong proficiency in Python and data analysis libraries to manipulate, clean, and extract insights from high-velocity data streams. • Security Awareness: Familiarity with cybersecurity concepts, threat detection principles, or common security data formats (such as system logs, event tracking, or endpoint telemetry). Preferred Qualifications & Advantages • SLM & Local Deployment: Direct experience working with, optimizing, and deploying Small Language Models (SLMs) to run efficiently in local or resource-constrained environments. • Analytical Thinker: Exceptional problem-solving skills with the ability to diagnose complex system behaviors and iteratively improve AI model performance based on data. • Adaptability: Passion for staying at the absolute forefront of rapidly evolving AI technologies and applying them creatively to complex domains. • Experience: 3+ years of professional experience in data science, AI engineering, or related technical fields, with a proven track record of deploying AI-driven solutions. • Education: Bachelor’s degree in Computer Science, Data Science, Cybersecurity, or a related quantitative field (or equivalent practical experience) from a recognized and accredited university.

Description CodeValue is looking for a hands-on Senior AI Engineer with real-world experience building and deploying LLM-based and Agentic Systems to production. You will be responsible for taking AI solutions from concept to reliable, scalable production systems, working across AI, backend, cloud, and infrastructure. Responsibilities • Design, develop, and deploy LLM-based and Agentic Systems for production environments. • Take AI/Agentic solutions from PoC and concept stages to stable, scalable, and reliable production systems. • Develop backend services and AI solutions using Python. • Work hands-on with agentic frameworks such as LangGraph, CrewAI, or similar/custom frameworks. • Deploy and operate AI systems in cloud environments. • Troubleshoot complex system issues and improve observability, reliability, and performance. • Collaborate with development, DevOps, security, and other technical teams. Requirements • · 5+ years of hands-on experience in product-based tech companies, with a focus on AI/ML systems. • · Proven track record in designing and implementing GenAI-powered solutions using LLMs and transformer-based architectures. • · Deep understanding of prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and LLM-based workflows. • · Strong experience with Azure Cognitive Services. • · Proficiency in Python or other relevant languages (e.g., TypeScript, Go), including use of AI/ML frameworks (e.g., LangChain, Hugging Face, PyTorch). • · Familiarity with containerized environments and orchestration tools (e.g., Docker, Kubernetes). • · Comfortable working with multi-cloud environments (Azure, AWS, or GCP). • · Experience with Agile/Scrum development methodologies. • · Strong communication skills and fluency in English (spoken and written). • • Preferred Qualifications/ Skills • · Experience with building high-performance distributed systems. • · Familiarity with agentic frameworks for multi-step reasoning or workflow automation. • · Exposure to AI model governance, data compliance, and MLOps pipelines. • · Background in Computer Science or related technical degree.

Realize your potential by joining the leading performance-driven advertising company! As a Senior GenAI Engineer in our GenAI team in the Tel Aviv office, you’ll play a key role in developing and enhancing our generative AI products, building advanced algorithmic solutions that improve content discovery, personalization, and monetization at scale. To thrive in this role, you’ll need: • M.Sc. in Computer Science, Mathematics, Engineering, or a related field • Strong Python, SQL and Spark skills • Familiarity with statistical modeling techniques • 2+ years of hands-on experience designing, building and evaluating GenAI workflows, including Agentic RAG, Tool calls evaluation, LLM-as-a-judge, RLHF, and LLM fine-tuning • 3+ years of experience in data science, analytics, and visualization • Excellent problem-solving and critical-thinking abilities • 3+ years of hands-on experience developing traditional machine learning algorithms, and NLP models. Bonus points if you have: • Strong mentoring and public-speaking skills, with a passion for writing research papers and technical documentation and sharing knowledge at conferences. • Experienced in Java or Scala How you’ll make an impact: • Be responsible for the entire algorithmic lifecycle of our user facing multilingual GenAI product at scale DeeperDive: data analytics, offline and online evaluation of new LLMs, evaluate the quality of our generative content, minimize hallucinations, evaluate guardrails, tool use and router calls, and improve retrieval combined with NER. • Improve personalized monetization flows and impact directly on the company’s revenue. • Work on a daily basis with on prem LLMs hosted in our datacenters, cooperate with external providers and embrace SOTA technologies. • Apply your scientific knowledge and creativity to analyze large volumes of diverse data and develop algorithmic solutions and models to solve complex problems • Influence directly on the way billions of people discover the internet. • Work on projects such as DeeperDive, LLM monetization, Personalized user questions, organic recommendations and personalized ads. Our tech stack: Python, Java, Langfuse, TensorFlow, Spark, Kafka, Cassandra, HDFS, iceberg, ElasticSearch, AirFlow, BigQuery, Google Cloud Platform, Kubernetes and Docker. Why Taboola? If you ask Taboolars what they love about working here, they’ll tell you that they’ve been empowered to realize their full potential while growing and learning from and with smart and talented people. They’ll also share more about: • Adam Singolda, Taboola Founder and CEO: “You can copy anything from another business but you can’t copy a company’s culture.” • Well-being: Competitive benefits and well-being programs tailored to the region, including comprehensive health benefits, a fully stocked kitchen, and location-specific perks. • Flexibility: We offer a hybrid work schedule with 3 days in-office, with an option to come in more often if desired. • Work with some of the biggest names: Our publisher partners include Yahoo, Conde Nast, Fox Sports, NBCU, ESPN, CBS, and E! Online. Our advertiser clients include Wells Fargo, Honda, Pinterest, and Expedia. Ready to realize your potential? Taboola is an equal opportunity employer and we value diversity in all forms. We are committed to creating an inclusive environment for all employees and believe such an environment is critical for success. Employment is decided on the basis of qualifications, merit, and business need. Learn more about #TaboolaLife on LinkedIn, Facebook, Instagram, X, YouTube, & the Taboola Life Blog. About Taboola Taboola empowers businesses to grow through performance advertising technology that goes beyond search and social and delivers measurable outcomes at scale. Taboola works with thousands of businesses who advertise directly on Realize, Taboola’s powerful ad platform, reaching approximately 600M daily active users across some of the best publishers in the world. Publishers like NBC News, Yahoo, and OEMs such as Samsung, Xiaomi and others use Taboola’s technology to grow audience and revenue, enabling Realize to offer unique data, specialized algorithms, and unmatched scale. About DeeperDive DeeperDive is a 10M monthly active users RAG based generative AI answer engine designed specifically for trusted publishers. It delivers concise, real-time answers that reflect what audiences are actively reading - surfacing not only what happened, but why it matters. Built on Taboola’s “Pulse of the Internet” - insights from over 600 million daily users — DeeperDive helps publishers respond to trending questions and deliver meaningful context on the topics their readers care about most. Rather than replacing journalism, DeeperDive enhances it. It empowers newsrooms to produce credible, nuanced content at scale, while preserving each publisher’s unique editorial voice and brand integrity. #LI-DN1 #LI-Hybrid

Description Company Overview: Cellebrite’s (Nasdaq: CLBT) mission is to enable its global customers to protect and save lives by enhancing digital investigations and intelligence gathering to accelerate justice in communities around the world. Cellebrite’s AI-powered Digital Investigation Platform enables customers to lawfully access, collect, analyze and share digital evidence in legally sanctioned investigations while preserving data privacy. Thousands of public safety organizations, intelligence agencies and businesses rely on Cellebrite’s digital forensic and investigative solutions—available via cloud, on-premises and hybrid deployments—to close cases faster and safeguard communities. To learn more, visit us at www.cellebrite.com, https://investors.cellebrite.com/investors and find us on social media @Cellebrite. Position Overview: Join Cellebrite’s Forensic Research Group and take part in uncovering hidden data across mobile applications and cloud ecosystems. The team identifies, analyzes, and extracts forensic artifacts from mobile apps, APIs, cloud services, and raw binary data. The team specializes in black-box reverse engineering and advanced mobile analysis across Android and iOS. As an AI Engineer, you'll work hands-on with leading LLMs and the broader agentic ecosystem, applying cutting-edge frameworks and techniques to build, evaluate, and iterate on solutions. Your work will span the entire software lifecycle — from rapid prototypes and POCs to production-grade systems. Implement solid design, flexible implementation, strong telemetry, and production feedback loops. Your responsibilities will be: • Design and maintain a LangGraph-based Agent Harness optimized for online and offline/on-premise deployment, using hybrid LLM and deterministic layers to balance speed, memory, and costs. • ​Own the complete evaluation lifecycle, including datasets, benchmarking frameworks, and automated regression testing to ensure output accuracy. • ​Standardize tool-calling interfaces for forensic engines and collaborate with researchers to operationalize AI models into Cellebrite’s products. Requirements • Bachelor’s degree in computer science or an engineering-related field. • 6+ years of experience in software development. • Proven experience as a Python developer. • Experience with Agent Orchestration Framework (like LangGraph) • Experience with the Agentic AI architecture, MCP, and A2A. • Solid understanding of RAG pipelines, vector databases, embeddings, and retrieval techniques (chunking, indexing, filtering, relevance tuning) • Experience with AI evaluation frameworks, prompt testing, or offline/online quality measurement • Attention to detail, ability to multitask, and strong interpersonal skills. Preferred: • Experience in enterprise-scale application development in a SaaS environment. • Strong observability design across infrastructure and AI workloads (metrics, logging, tracing, AI quality signals) • Experience with an OOP language #LI-AS1