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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 Res 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 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.