Staff AI Engineer - Architecture skills

Role overview A senior, hands-on engineering role focused on shaping the architecture of agentic AI systems for an insurance-focused SaaS platform. The position sits at the intersection of applied LLM research and production-grade software engineering, with responsibility for designing, prototyping, and shipping next-generation AI capabilities. It is intended for a builder who wants to drive technical strategy while remaining deeply involved in coding and mentoring.

Responsibilities - Design, prototype, and build production-ready agentic workflows, custom orchestration layers, and tool-calling integrations using modern AI frameworks. - Transition AI prototypes into robust, low-latency microservices on a major cloud platform, ensuring tight integration with the surrounding tech stack. - Integrate emerging paradigms such as Model Context Protocol, advanced retrieval-augmented generation patterns, vector pipelines, and structured LLM outputs into the production stack. - Establish LLMOps practices covering experiment tracking, evaluation, prompt engineering, latency optimization, token budget governance, and model observability. - Mentor engineering squads, lead technical design reviews, set coding standards, and raise the team's overall AI engineering capability. - Continuously benchmark new models, tools, and architectures, translating relevant breakthroughs into practical product value.

Requirements - Proven, hands-on experience building with agentic frameworks such as LangGraph, LlamaIndex, OpenAI Agents SDK, Microsoft Agent Framework, or custom orchestration solutions. - Practical expertise with Model Context Protocol, tool and function calling, structured LLM outputs, and complex RAG pipelines including vector retrieval, hybrid search, and embeddings. - Experience with LLMOps tooling, including evaluation frameworks, tracing, prompt engineering, and compute or token governance in production. - Strong proficiency in Python and object-oriented programming for AI ecosystem work. - Track record building and scaling backend microservices and REST or OpenAPI endpoints on enterprise cloud infrastructure, preferably Azure. - Familiarity with vector and relational databases, event-driven patterns, containerization with Docker and Kubernetes, CI/CD, and infrastructure-as-code practices.

Nice to have - Comfort reading research papers and extracting practical product value from emerging models and libraries. - Demonstrated ability to upskill peers through pair programming, code reviews, and pragmatic design discussions.

Benefits and work setup - Flexible remote and hybrid working options. - Competitive base salary plus a variable component tied to personal and company performance. - Multiple learning and development opportunities, including dedicated half-day learning sessions each month. - Generous paid time off and paid holidays. - Mental health benefits and paid volunteering days each year. - Additional country-specific benefits may apply.