AI Engineer jobs in Israel

Our Mission At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place. Who We Are In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us! We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes. Job Summary Join our Security Autonomous SOC Team as a Principal AI Engineer to transform innovative AI/LLM research into impactful cybersecurity product features. You will leverage and augment our existing infrastructure, including BI systems and CI/CD pipelines, to design, develop, and deploy advanced LLM and Agentic capabilities. Your expertise in software development, AI engineering, and modern LLM technologies will directly drive the evolution of our security operations. By building robust, production-ready AI solutions, you will make a tangible difference in protecting our digital world against complex threats. Key Responsibilities • Design and build Agentic security solutions to improve detection, response, and remediation capabilities. • Architect, implement, and optimize end-to-end LLM pipelines ensuring scalability from data ingestion to deployment. • Develop and deploy AI agents leveraging LLMs to automate complex security decision-making tasks. • Integrate LLM APIs and fine-tune models for specific cybersecurity use cases. • Transform promising AI and LLM research concepts into mature, robust, and scalable production-ready features. • Augment established BI data systems and CI/CD pipelines to support advanced AI development and experimentation. • Develop specialized tools and frameworks extending infrastructure to support AI-driven security research and remediation. • Collaborate with cross-functional teams to integrate AI innovations into customer-facing XDR, EDR, and SOAR platforms. Qualifications Required Qualifications • Minimum of 5 years of proven experience in software engineering and delivering production-ready solutions. • Demonstrated expertise designing, building, and optimizing end-to-end LLM pipelines and AI agents. • Exceptional programming skills in Python with a strong background in robust software development. • Extensive experience with data engineering, CI/CD environments, and cloud platforms like AWS, GCP, or Azure. • Proficiency in containerization technologies, including Docker and Kubernetes, for scaling AI and LLM solutions. • In-depth practical knowledge of AI concepts, LLM APIs, and model fine-tuning techniques. Preferred Qualifications • Familiarity with cybersecurity concepts, security operations, incident response, and advanced threat research. • Hands-on experience working with XDR, EDR, NDR, or SOAR products and understanding attack methods. • Proven ability to innovate independently while collaborating effectively within dynamic, cross-functional engineering teams. • Strong critical thinking mindset with a passion for applying cutting-edge AI to solve complex security challenges. Our Commitment We’re trailblazers that dream big, take risks, and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together. We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at [email protected]. Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics. All your information will be kept confidential according to EEO guidelines. Is role eligible for Immigration Sponsorship? No. Please note that we will not sponsor applicants for work visas for this position.

Our Mission At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place. Who We Are In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us! We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes. Job Summary Your Career: As a Principal AI Software Engineer on our Generative AI foundation team, you will build AI agents and shared infrastructure that power GenAI capabilities across the company. You will be involved in the full software lifecycle, from fast-moving prototypes to production-grade systems, enabling CyberArk’s product lines to adopt agentic workflows safely and at scale. You will collaborate with product managers, researchers, and engineers to translate ideas into robust services, bringing a pragmatic mindset to experimentation, iteration, and operational excellence. Your Impact: • Engage in the full software lifecycle, from fast-moving prototypes and POCs to production-grade, observable systems. • Collaborate with product managers, researchers, and engineers to translate ideas into robust services. • Work hands-on with leading LLMs and the modern agentic ecosystem, leveraging a wide range of AWS services. • Use cutting-edge frameworks and techniques to develop, evaluate, and continuously improve solutions. • Implement solid design, flexible implementation, strong telemetry, and production feedback loops. Qualifications Your Experience: • Bachelor’s degree in computer science or an engineering-related field. • 8+ years of experience in software development. • Experience with cloud infrastructure (e.g., AWS, GCP, Azure). • Proven experience as a Python developer. • Meaningful experience developing AI products with LLMs, including solution architecture, development and prompt engineering. • Attention to detail, ability to multitask, and strong interpersonal skills. Preferred Qualifications • Specific experience with AWS. • Experience with the Agentic AI architecture, MCP, and A2A. • Experience in enterprise-scale application development in a SaaS environment. Our Commitment We’re trailblazers that dream big, take risks, and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together. We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at [email protected]. Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics. All your information will be kept confidential according to EEO guidelines. Is role eligible for Immigration Sponsorship? No. Please note that we will not sponsor applicants for work visas for this position.

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.

Description We Are KPMG Israel. KPMG Israel is more than a professional services firm, we are a community of people driven by impact. As part of a leading global network, we partner with the most influential organizations to drive large-scale business and technology transformation, combining strategic thinking, innovation, and advanced technologies. Guided by integrity, excellence, and forward thinking, we help organizations navigate complexity and create real change. Our people are at the center, with space to lead, experiment, and grow in a dynamic and collaborative environment that connects personal development with meaningful impact on the future of business and society. We are seeking an experienced Generative AI Engineer to join our AI squad at KPMG. This is a unique opportunity to wear multiple hats - serving as both a developer of cutting-edge GenAI solutions and an advisory expert helping organizations transform their AI capabilities. You'll build end-to-end GenAI projects from conception to production while staying at the forefront of this rapidly evolving field. Key Responsibilities GenAI Development & Implementation • End-to-End Development: Build GenAI solutions from POC through production deployment, handling all backend development responsibilities • Client Engagement: Participate in technical discussions with clients, gather requirements, and help translate business visions into feasible technical solutions through presentations and consultations • Backend Development: Design and implement production-grade microservices architectures for GenAI applications using Python • Cloud Implementation: Deploy and manage GenAI solutions across GCP, Azure, and AWS platforms, leveraging cloud-native AI services • Cross-functional Collaboration: Work closely with project managers, full-stack developers, and Power Automate teams to deliver complete solutions • System Evaluation: Assess and optimize production-grade GenAI systems for performance, scalability, and reliability Continuous Learning & Innovation • Technology Scouting: Continuously explore and evaluate new GenAI models, frameworks, and techniques as they emerge • Best Practices Development: Establish and refine methodologies for GenAI solution development and deployment Requirements Technical Expertise: • Programming: Advanced proficiency in Python for backend development and AI applications • GenAI Mastery: Deep understanding of large language models (LLMs) and experience with major model APIs (OpenAI, Anthropic, Google, etc.) • Multi-Agent Systems: Expertise in designing and implementing GenAI multi-agent architectures • Prompt Engineering: Advanced skills in prompt design, optimization, and engineering techniques • Cloud Platforms: • Required: Hands-on experience with AI services in at least one major cloud platform (GCP, Azure, or AWS) • Advantage: Experience across multiple cloud platforms (AI Search, Vertex AI, SageMaker, etc.) • Development Frameworks: Experience with GenAI frameworks like LangChain and cloud-based retrieval services • Software Engineering: Strong background in microservices architecture, API development, and production system design • AI/ML Fundamentals: Solid understanding of deep learning principles and GenAI techniques • Containerization (Advantage): Experience with Docker and Kubernetes for deployment and orchestration • OCR Technologies (Advantage): Experience with Optical Character Recognition systems and document processing • Data Pipelines (Advantage): Experience building and maintaining data processing pipelines Professional Experience • Mid+ Level Experience: 2+ years in AI/ML development with significant GenAI project experience • Production Systems: Proven track record of deploying and maintaining AI solutions in production environments • Client-Facing Experience: Comfortable with technical presentations and requirement gathering sessions Education & Background • Preferred: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related technical field • Alternative: Demonstrated industrial experience in developing deep learning and GenAI solutions (degree not required with strong portfolio) Soft Skills • Problem-Solving: Excellent analytical and creative problem-solving abilities • Communication: Strong technical communication skills for both technical and non-technical audiences • Collaboration: Proven ability to work effectively in cross-functional teams • Adaptability: Thrives in fast-paced environments and eager to learn emerging technologies • Consulting Mindset: Ability to understand client needs and provide strategic technical guidance What We Offer • Opportunity to work on cutting-edge GenAI projects across diverse industries • Dual role combining hands-on development with strategic consulting • Access to latest AI technologies and cloud platforms • Collaborative environment with cross-functional teams • Professional development in a rapidly evolving field Diversity & Inclusion This position is open to all qualified candidates. We support diversity and view it as a strength that enhances our ability to develop, learn, and innovate. We advocate for inclusion and empower people of all backgrounds to contribute their unique perspectives.

ScaleOps is redefining autonomous cloud and AI infrastructure. We're on a mission to free DevOps and platform engineers from manual resource management so they can focus on innovation, not tuning resources. The results: maximized performance and a reduction of cloud costs by up to 80%. As the category leader in Autonomous Cloud and AI Infrastructure Resource Management, we're trusted by leading enterprises including Adobe, Wiz, Epic Games, Northwestern Mutual, Coinbase, DocuSign, and Fortune 100 companies to autonomously manage their most critical production environments. Backed by Insight Partners, Lightspeed Venture Partners, and other leading VCs with over $210M in funding, ScaleOps is the leading player in a massive and growing market. We are building the autonomous infrastructure management platform that will power the next decade of enterprise compute. What You'll Be Doing • Lead the AI Platform team: own the internal platform ScaleOps R&D builds on, including CI/CD, Kubernetes tooling, developer experience, and infrastructure automation • Stay hands-on while leading the team day to day: delivery, technical decisions, and hiring • Build and connect AI tools for R&D and the company, and bring new tools and best practices from the market into how we build the product • Build infrastructure for the other R&D teams, from internal agentic platforms to the foundations that LLM-powered automation runs on • Partner closely with backend, product, and R&D teams, and treat engineers as your customers: measure adoption and time saved • Identify and eliminate friction in the development lifecycle through automation and self-service tooling What You'll Bring • 5+ years of hands-on experience in platform engineering, infrastructure, or AI/ML infrastructure in high-scale environments • 2+ years of proven experience leading software engineering initiatives and mentoring engineers in a production environment • Experience building self-service platforms or tooling that other engineers adopted • Strong hands-on experience with Kubernetes, cloud (AWS, GCP, or Azure), and infrastructure-as-code • Strong development skills: you have written production code, ideally Go, such as Kubernetes operators or controllers • A builder mindset, strong ownership, and curiosity about the AI market and its tooling • Full professional fluency in Hebrew and English (ב-US: “Full professional fluency in English”) • (Nice to have) Open-source contributions in the cloud-native space (Karpenter, Cilium, KEDA, Argo, Kueue) • (Nice to have) Experience with LLM serving, agentic frameworks, or MCP-based tooling • (Nice to have) Experience with scheduling, node lifecycle, or resource management in Kubernetes

ScaleOps is redefining autonomous cloud and AI infrastructure. We're on a mission to free DevOps and platform engineers from manual resource management so they can focus on innovation, not tuning resources. The results: maximized performance and a reduction of cloud costs by up to 80%. As the category leader in Autonomous Cloud and AI Infrastructure Resource Management, we're trusted by leading enterprises including Adobe, Wiz, Epic Games, Northwestern Mutual, Coinbase, DocuSign, and Fortune 100 companies to autonomously manage their most critical production environments. Backed by Insight Partners, Lightspeed Venture Partners, and other leading VCs with over $210M in funding, ScaleOps is the leading player in a massive and growing market. We are building the autonomous infrastructure management platform that will power the next decade of enterprise compute. What You'll Be Doing • Build and run the internal platform ScaleOps R&D builds on: CI/CD, Kubernetes tooling, developer experience, and infrastructure automation • Give every engineer a fast, safe path to develop, test, and ship code to production • Build and connect AI tools for R&D and the company, and bring new tools and best practices from the market into how we build the product • Build infrastructure for the other R&D teams, from internal agentic platforms to the foundations that LLM-powered automation runs on • Partner closely with backend, product, and R&D teams, and treat engineers as your customers: measure adoption and time saved • Identify and eliminate friction in the development lifecycle through automation and self-service tooling What You'll Bring • 5+ years of hands-on experience in platform engineering, infrastructure, or AI/ML infrastructure in high-scale environments • Experience building self-service platforms or tooling that other engineers adopted • Strong hands-on experience with Kubernetes, cloud (AWS, GCP, or Azure), and infrastructure-as-code • Strong development skills: you have written production code, ideally Go, such as Kubernetes operators or controllers • A builder mindset, strong ownership, and curiosity about the AI market and its tooling • Full professional fluency in Hebrew and English (ב-US: “Full professional fluency in English”) • (Nice to have) Open-source contributions in the cloud-native space (Karpenter, Cilium, KEDA, Argo, Kueue) • (Nice to have) Experience with LLM serving, agentic frameworks, or MCP-based tooling • (Nice to have) Experience with scheduling, node lifecycle, or resource management in Kubernetes

ScaleOps is redefining autonomous cloud and AI infrastructure. We're on a mission to free DevOps and platform engineers from manual resource management so they can focus on innovation, not tuning resources. The results: maximized performance and a reduction of cloud costs by up to 80%. As the category leader in Autonomous Cloud and AI Infrastructure Resource Management, we're trusted by leading enterprises including Adobe, Wiz, Epic Games, Northwestern Mutual, Coinbase, DocuSign, and Fortune 100 companies to autonomously manage their most critical production environments. Backed by Insight Partners, Lightspeed Venture Partners, and other leading VCs with over $210M in funding, ScaleOps is the leading player in a massive and growing market. We are building the autonomous infrastructure management platform that will power the next decade of enterprise compute. What You'll Be Doing We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the ScaleOps platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales. • Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production. • Platform Integration & Intelligent Decision Systems: Develop MCPs to expose ScaleOps capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?" • AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving. • R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support. • AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations. • End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production. • Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how ScaleOps evolves from AI-enhanced internal tools to customer-facing AI products. What You'll Bring • Core Engineering: Significant software engineering experience (typically 4+ years) with strong Python skills and solid backend engineering fundamentals. • Production Experience: Experience building and operating production systems in cloud environments. • Real-World GenAI Experience: Practical experience bringing LLM-based systems into production, including handling latency, cost control, and failure modes. Familiarity with additional agentic frameworks (e.g., LangChain, MetaGPT) and evaluation frameworks. • Builder Mentality: Strong ownership and the ability to operate independently while collaborating closely across teams, with the motivation to grow into technical leadership as the group expands. • (Advantage) Data & RAG: Experience enabling LLMs to consume structured or operational data (configurations, logs, metrics) and experience with retrieval systems (RAG) or vector databases.

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.

Who We Are Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology. What We Offer Location: Rehovot,ISR You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more. At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits. We are looking for an experienced Senior Software Engineer to build the calibration systems software for advanced electron microscope columns. The role You will own the architecture, design, and implementation of software that combines scientific computing, image processing, and distributed systems to enable fully automated calibration workflows - working at the intersection of software engineering, physics, and real laboratory hardware, under stringent performance requirements. The team is part of the R&D department behind a major new product, developing the next generation of software and algorithm solutions that will power Applied Materials' metrology and inspection systems. What you'll be doing: • Stay deeply hands-on while owning project-level architecture for complex, stateful calibration workflows. • Build end-to-end, full-stack services - from concurrent, iterative hardware control loops up to the operator-facing UI. • Own algorithms end to end - research, design, implement, validate, document, optimize - across numerical methods, computer vision (registration, pattern matching, tracking), and performance-critical real-time image processing. • Apply data science methods to real measurement data: design experiments, analyze results, quantify system performance with sound metrics. • Build and evolve the AI-specific infrastructure of our physics-informed SDLC harness-agents, tools, context and evaluation pipelines. • Develop hardware simulation and monitoring tools that support the team algorithm development and automated testing. • Work closely with physicists to validate and refine calibration workflows and algorithms, and with Product, Design, and other R&D teams to ship them. What you should have: • 5+ years of professional software development • 2+ years of R&D experience (algorithms / ML), including end-to-end ownership of meaningful features and projects. • Deep concurrency expertise: designing concurrent execution models, parallelism, and multi-threaded dispatchers - and reasoning about their failure modes, tradeoffs, and performance under load. • Strong system design: microservices and distributed systems, API design, message queues or event streaming (delivery semantics, ordering, failure handling). • Numerical algorithms and optimization, image processing fundamentals, and familiarity with hardware control or instrumentation software. • AI engineering: hands-on experience building with LLMs and agents - prompt and context engineering, tool calling, retrieval, evaluation - and a genuine drive to stay current. • Solid engineering practices: e2e, regression, and automated testing, code review, CI/CD, virtualization (Docker). • A product mindset - you understand customer needs and business impact. • B.Sc. in Computer Science, Engineering, or a related field, or equivalent industry experience. Preferred backgrounds: computational physics, applied mathematics, image processing, optics, microscopy, signal processing, numerical analysis. Nice to have: • M.Sc. in CS, Software Engineering, EE, or a related field. • A proven track record of excellence - academic, military, or professional honors, awards, or recognition for outstanding achievements. • Electron microscopy or semiconductor equipment. • Experience with advanced performance profiling and optimization. • Experience with advanced concurrency models in another backend language (C++, Java, Kotlin, Go, Rust). Who we are: Applied Materials Israel is home to the Process Diagnostics and Control business unit (PDC). Based in Rehovot, we develop, manufacture, and market cutting-edge machine learning and computer vision-based metrology and inspection products that are essential elements in wafer fabrication. By playing a significant role in enabling the production of the next generation of microchips, our expertise enables our customers to transform possibilities into reality. Additional Information Time Type: Full time Employee Type: Assignee / Regular Travel: No Relocation Eligible: No Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.

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