Lead AI Engineer

What you will do
• Build GenAI solutions for insurance. Design and ship production-grade LLM and generative AI
features that solve real underwriting, claims and policy-servicing problems.
• Create agentic workflows. Develop AI agents with tool calling, orchestration and multi-step
reasoning that can run reliably inside business processes.
• Automate document work. Build capabilitiesfor document classification, data extraction and
summarisation.
• Choose the right model. Assess various models from Azure AI define routing strategies that
balance quality, latency, reliability and cost.
• Measure quality. Set up evaluation suites,structured outputs and guardrails, and run automated
regression tests so quality stays visible as the product changes.
• Tune and operate. Optimise for latency, token usage and scale; monitor production systems and
lead root-cause analysis when quality or performance slips.
• Integrate and collaborate. Connect AIservicesto backend APIs, databases and enterprise
workflows alongside Core Engineering, Product and Solutions teams.
• Lead the team. Mentor AI engineers, shape engineering standards, and keep evaluating new
models and frameworks for fit.
What you will bring
• Python and applied AI:strong hands-on delivery of AI/ML applications.
• GenAI foundations: LLMs, agents and function calling.
• Quality engineering: prompt design,structured outputs, guardrails, evaluation and AI
observability.
• Azure AIstack: Azure AI, Microsoft Foundry and Azure OpenAI.
• Agentframeworks: Microsoft Agent Framework, LangGraph, Semantic Kernel orsimilar.
• Foundation models: working experience with OpenAI, Anthropic Claude or comparable providers.
• Backend skills: REST APIs, asynchronous processing, PostgreSQL (vectorsearch is a plus).
• Delivery tooling: Git, Docker, CI/CD with Azure DevOps or GitHub Actions, and AI-assisted
development tools such as Claude Code and GitHub Copilot.