Unixi

Unixi is rethinking Identity and Access Management (IAM) for the modern enterprise. Our Universal SSO approach enables organizations to control access across SaaS applications without the heavy integration burden and cost model of legacy IAM platforms. Combined with SaaS discovery, governance, and growing lifecycle capabilities, Unixi gives security and IT teams a new way to manage identity across the real SaaS world. With the introduction of Lifecycle Management (LCM), Unixi is evolving into a broader IAM platform, one that not only helps organizations control access, but also automate identity operations, govern SaaS usage, and manage user lifecycle workflows across their environment. We're a fast-growing, well-funded cybersecurity startup backed by leading investors and industry veterans, and we're looking for a Senior Backend Engineer, Identity Platform to help us build the core systems behind the platform. As a Senior Backend Engineer at Unixi, you'll work on deep backend and product challenges at the heart of modern identity. You will design and build the services, APIs, and internal systems that power authentication, policy enforcement, lifecycle workflows, SaaS integrations, and the operational backbone of our platform. This is a high-impact role for an engineer who wants to build real product, own production systems, and help shape the technical foundations of a new kind of IAM company. This is not a narrow implementation role. We are looking for someone with strong backend fundamentals, sound engineering judgment, and the ability to move from architecture and design all the way to production. Responsibilities: Design, build, and maintain scalable backend services, APIs, and internal systems that power Unixi's identity platform. Own features end to end – from technical design and implementation to testing, deployment, and production support. Build core platform capabilities across authentication, policy enforcement, SaaS discovery, lifecycle workflows, and enterprise integrations. Work on complex backend challenges involving scale, reliability, performance, data modeling, and multi-system orchestration. Design robust integrations with external platforms such as SaaS applications, identity providers, and enterprise systems. Collaborate closely with product, frontend, and platform engineers to translate product requirements into strong backend architecture. Improve system observability, maintainability, testing practices, and operational excellence across the backend stack. Contribute to technical direction and help shape the architecture of a growing cybersecurity platform. Requirements: 5+ years of backend software engineering experience. Strong hands-on experience with Python and excellent backend engineering fundamentals. Experience designing and building production-grade backend systems and APIs. Strong understanding of distributed systems, asynchronous processing, and backend reliability concerns. Experience working with relational databases; PostgreSQL experience is a strong advantage. Experience integrating with external APIs and building reliable service-to-service workflows. Experience working in cloud production environments such as GCP, AWS, or Azure. Familiarity with containerized deployment environments; Kubernetes experience is a plus. Strong debugging and problem-solving skills, with a clear production ownership mindset. Strong communication skills, high ownership, and the ability to thrive in a startup environment. Bonus: Experience with authentication, authorization, SSO, SAML, SCIM, lifecycle management, or related identity technologies. Experience with Google Cloud and cloud-native backend systems. Interest in solving hard enterprise security problems with elegant product and engineering solutions. Nice to Have: Experience working on products that include a browser extension layer, or backend systems tightly coupled to browser behavior, authentication flows, and web session management – big advantage. Experience in IAM, cybersecurity, SaaS management, enterprise networking or enterprise infrastructure domains – big advantage. Experience building security-sensitive or reliability-critical backend systems. Startup experience, especially in early-stage or scaling product companies. Ready to make IAM better and more secure? Apply now and join our mission at Unixi!

Unixi is revolutionizing Identity and Access Management (IAM) with our Universal SSO solution that seamlessly works with SaaS applications-without requiring traditional integrations. By removing complexity, eliminating the SSO tax, and giving organizations visibility and control over their SaaS ecosystem, Unixi is building a new kind of IAM platform for the modern enterprise. Now, with the introduction of Lifecycle Management (LCM), Unixi is expanding from a breakthrough access solution into a broader IAM platform – one that not only controls access, but helps organizations manage identity workflows and user lifecycle processes across their SaaS environment. We're a fast-growing, well-funded startup backed by industry-leading investors and cybersecurity pioneers, and we're looking for an exceptional AI Software Engineer / AI Agent Engineer to help us turn AI-driven product ideas into reliable, production-grade systems. As an AI Software Engineer at Unixi, you'll work on one of the most interesting problems in enterprise software: applying AI agents and multi-step workflows to real operational challenges in identity, access, and lifecycle management. This is not a research role. It is a hands-on engineering role for someone who loves building working systems end to end, from workflow design and backend integration to evaluation, reliability, and production readiness. You will help us increase our execution capacity around AI-driven product capabilities and remove bottlenecks in integrating, testing, and operationalizing production AI workflows. This is an opportunity to join early, work closely with product and engineering leadership, and help define how AI becomes a real part of the product rather than just a prototype. Responsibilities: Design and implement multi-step AI workflows and agentic systems for real product use cases. Integrate AI workflows with backend services, internal systems, external APIs, databases, and product data sources. Define runtime behavior, state management, retries, failure handling, and fallback logic for production-grade AI systems. Build ingestion and output flows for AI-driven product capabilities, including structured and unstructured data processing. Test, evaluate, and improve the quality, reliability, and determinism of AI workflows in production environments. Improve observability, tracing, maintainability, and operational readiness of AI systems. Collaborate closely with backend engineers, product leaders, and domain experts to turn ambiguous ideas into robust implementations. Help shape engineering best practices for agent orchestration, tool usage, security guardrails, and production AI architecture. Requirements: 4+ years of software engineering experience, with strong backend development skills. Strong Python experience and solid software engineering fundamentals. Hands-on experience building AI agents, LLM workflows, or orchestration systems in production or near-production environments. Experience with frameworks such as Google ADK, LangGraph, LangChain, or similar agent/workflow orchestration tools. Experience with Vertex AI or comparable cloud AI platforms. Experience integrating AI systems with APIs, databases, backend services, and external tools. Strong understanding of workflow execution concerns such as state handling, retries, failure recovery, and reliability. Experience testing and validating AI-driven systems beyond simple prompting or demos. Ability to move quickly, own problems end to end, and build production-quality systems in a startup environment. Strong communication skills and a practical, execution-focused mindset. Bonus: Background in cybersecurity, IAM, networking, or enterprise SaaS products – big advantage. Startup experience, especially in early-stage or 0-to-1 environments – big advantage. Experience with evaluations, prompt/version testing, and quality measurement for AI systems. Experience with observability, tracing, and monitoring tools for AI workflows. Familiarity with tool ecosystems, MCP-style integrations, or structured tool-calling architectures. Experience with browser automation, scraping, or complex data extraction workflows. Experience with multimodal or document-based workflows. Experience with security, guardrails, or abuse prevention for AI systems. Experience optimizing cost, latency, and performance of LLM-based production systems. Ready to make IAM better and more secure? Apply now and join our mission at Unixi!