Lead AI Engineer

Lead AI Developer

About The Role As a Lead AI Developer, you will play a key role in shaping and delivering AI-enabled solutions that transform how engineering teams, business users, and cross-functional stakeholders work across Property & Casualty Reinsurance.

You will be responsible for designing, building, and scaling AI-powered applications, agentic workflows, intelligent automation capabilities, and enterprise-ready GenAI solutions. The role requires a strong hands-on engineering mindset, deep technical understanding of modern AI platforms, and the ability to convert business and engineering challenges into practical, secure, scalable, and reusable AI solutions.

This is not only a development role. You will act as a technical lead and AI engineering SME, helping teams adopt AI responsibly across the software delivery lifecycle, business processes, knowledge discovery, decision support, and productivity improvement use cases. You will work closely with product owners, architects, engineering teams, data specialists, business stakeholders, and platform teams to identify high-value opportunities and deliver solutions that are reliable, measurable, and aligned with Swiss Re’s engineering and governance standards.

As this is a hands-on engineering role, you will also build, evolve and run the software, services and workflows that LLM-based agents use. You will work day-to-day with agentic coding harnesses: breaking work into well-scoped tasks, directing AI agents to build and modify code, and reviewing their output with calibrated scrutiny — spec-first, tested, and accountable for what your agents produce

You will also help establish patterns, standards, guardrails, reusable components, and best practices that enable AI adoption across multiple P&C Re teams and domains.

Key Responsibilities

• Lead the design and development of scalable AI-powered applications, GenAI solutions, intelligent assistants, and agentic workflows for P&C Re.
• Build agentic-friendly software: the tools, services and workflows that LLM-based agents call
• Design AI workflows that combine models, prompts, tools, enterprise data, and business logic
• Validate model behavior, outputs, and assumptions from a production and business-use perspective
• Design and maintain evaluation frameworks for AI/LLM systems, including test suites and human evaluation workflows
• Write evaluations and quality checks for agent behavior, and build in grounding and assurance
• Ensure reliability, scalability, cost control, and latency optimization
• Help bring new agentic systems into production

Key Skills and Expertise

• Core Engineering Mindset and Enterprise-grade AI capabilities using modern technologies such as Python, Java, TypeScript, Azure AI Foundry, Azure OpenAI, GitHub Copilot, Lang Chain, Palantir, Claude code, Semantic Kernel, or similar frameworks.
• Core full-stack engineering capabilities to build AI solutions that can support practical business and engineering use cases such as knowledge discovery, document intelligence, process automation, engineering productivity, code assistance, testing support, operational insights, and decision support.
• Architectural Mindset to implement robust Retrieval-Augmented Generation solutions, including data ingestion, chunking strategies, embeddings, vector search, prompt orchestration, grounding, source attribution, and response quality controls.
• Demonstrated Enterprise Integration Experience to develop and integrate AI agents and LLM-based workflows with enterprise systems such as Azure DevOps, Jira, Confluence, SharePoint, Palantir Foundry, Databricks, internal APIs, and business applications where applicable.
• Collaboration and communication skills to interact and strategize technology roadmap with product owners, business SMEs, architects, data engineers, platform teams, and engineering squads to understand business needs and translate them into secure, scalable, and maintainable AI solutions.
• Ability to establish strong engineering practices for AI development, including prompt design, evaluation frameworks, automated testing, observability, monitoring, versioning, CI/CD, and production support.
• Demonstrated engineering aspects for technical troubleshooting across complex AI solution issues, including model behavior, hallucination risk, grounding gaps, latency, integration failures, data quality issues, and production reliability concerns…