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.

• Leadership skills to mentor developers and engineers on AI development patterns, prompt engineering, agentic development, secure integration, testing approaches, and responsible AI practices.

• Evolving mindset to contribute towards broader AI adoption journey in P&C Re by identifying high-impact use cases, supporting pilots, sharing learnings, and helping scale successful solutions across teams.

About the Team

We are a team that believes in engineering excellence, practical innovation, and responsible use of technology. Our leaders are expected to stay close to engineering and lead by example through strong technical judgement, hands-on contribution, and high-quality delivery.

In P&C Re, AI is becoming an important part of how we improve engineering productivity, business processes, decision support, and user experience. We are focused on applying AI where it creates real value, with clear guardrails, strong ownership, and close collaboration between business and technology teams.

Quality, stability, security, and responsible delivery are first-class expectations in everything we do. We work closely with product owners, business stakeholders, architects, engineers, data specialists, and platform teams to build solutions that are thoughtfully designed, well-engineered, and sustainable over time.

About You

You are an experienced AI-focused full stack engineer who enjoys solving complex problems and turning emerging technologies into practical, enterprise-ready solutions. You are hands-on, curious, collaborative, and comfortable working across engineering, architecture, product, and business teams.

You understand that successful AI adoption is about integrating an LLM. It requires strong software engineering, reliable data flows, thoughtful user experience, responsible AI controls, measurable outcomes, and continuous learning from real usage.

You are comfortable leading technical discussions, making architecture decisions, mentoring engineers, and helping teams adopt new ways of working. You are pragmatic, outcome-oriented, and able to balance innovation with security, governance, maintainability, and delivery discipline.

Minimum Requirements

• Bachelor's degree in computer science, Engineering, Information Technology, Data Science, or a related field, with 12+ years of software engineering experience, including significant hands-on experience in AI, GenAI, automation, or intelligent application development.

• Strong hands-on development experience in one or more programming languages such as Python, Java, or TypeScript, with the ability to build production-grade applications and services.

• Proven experience designing and delivering AI-powered applications, LLM-based solutions, intelligent assistants, automation capabilities, or agentic workflows in an enterprise environment.

• Strong understanding of modern GenAI concepts such as prompt engineering, function calling, tool usage, RAG, embeddings, vector databases, grounding, semantic search, AI agents, orchestration, and evaluation.

• Experience with AI and cloud platforms such as Azure AI Foundry, Azure OpenAI, Azure services, GitHub Copilot, Palantir Foundry AIP, Databricks, AWS Bedrock, Google Vertex AI, Claude Code or similar platforms.

• Strong software engineering foundation, including API design, microservices, integration patterns, secure coding, automated testing, CI/CD, observability, and production support.

• Experience integrating AI solutions with enterprise data sources, internal applications, APIs, document repositories, workflow systems, knowledge bases, or engineering platforms.

• Ability to design scalable and secure solution architectures for AI use cases, including access control, data privacy, auditability, monitoring, and responsible AI guardrails.

• Experience implementing quality and reliability practices for AI solutions, including response evaluation, regression testing, hallucination mitigation, traceability, feedback loops, and performance monitoring.

• Strong understanding of data structures, data pipelines, APIs, metadata, and enterprise information architecture required to support AI applications.

• Proven ability to troubleshoot complex technical issues across application code, AI model behavior, data quality, integrations, infrastructure, and user workflows.

• Strong experience partnering with product owners, architects, business stakeholders, data engineers, analysts, and engineering teams to translate business needs into practical AI-enabled solutions.

• Demonstrated ability to lead technical initiatives, make architectural decisions, mentor engineers, define engineering standards, and promote reusable best practices across teams.

• Demonstrated ability to act as an AI Engineering SME across multiple teams, projects, or business domains, with the ability to influence engineering practices beyond a single delivery team.

• Ability to evaluate emerging AI technologies and recommend practical improvements to enterprise platforms, tools, engineering workflows, and delivery practices.

• Experience with agentic AI development, including multi-step workflows, tool-using agents, task decomposition, autonomous execution patterns, and human approval flows.

• Strong documentation skills, including the ability to create architecture documents, technical specifications, implementation guidance, process documents, standards, and reusable playbooks.