Machine learning engineer in insurance

Insurance isn’t the first industry most engineers think of when they imagine cutting-edge AI work. That’s exactly why this role is interesting. CFC’s Data & AI unit is building production agentic systems that automate complex underwriting decisions - not chatbots, not copilots bolted onto legacy workflows, but autonomous multi-step AI agents that reason over unstructured data, assess risk, and drive real business outcomes. We’re using frameworks like LangChain to orchestrate LLM-driven services that sit at the heart of how the business operates.

• Design, develop, and maintain business-critical AI agent services.
• Build tailored agent workflows and services from business requirements, using LangChain and related frameworks with reliable patterns for LLM-driven decision-making in production.
• Integrate tests and validation to improve AI agents through evals and monitoring.
• Work with software engineers and architects to lead system design and architectural decisions.
• Translate technical specifications into clean, testable, and scalable production code.
• Work closely with cross-functional teams - engineers, data scientists, product managers - to deliver features on time and to a high standard.
• Write unit and integration tests to maintain reliability and service correctness.
• Monitor, troubleshoot, and continuously improve production services.
• Produce clear, structured documentation for systems, architecture, and processes.
• Mentor junior engineers through code reviews, best-practice guidance, and knowledge sharing.

• Significant experience building production-grade AI agents or LLM-powered services.
• Strong Python development experience, ideally with 6+ years of professional software engineering experience.
• A track record of writing clean, maintainable and high-quality production code.
• Experience supporting business-critical systems in live production environments.
• Strong understanding of asynchronous programming, Docker, containerised deployments and modern service architectures.
• Experience designing distributed systems, asynchronous microservices and event-driven architectures.
• Confidence leading system design discussions and making pragmatic architectural trade-offs.
• Strong cloud experience, ideally within Azure.
• Experience deploying, monitoring and maintaining ML or LLM models in production.
• Familiarity with MLOps, LLMOps, evals, monitoring and lifecycle management.
• Hands-on experience orchestrating LLM workflows using frameworks such as LangChain.
• An interest in how agentic software development can improve engineering delivery without compromising code quality.
• Strong communication skills and the ability to collaborate effectively in remote or asynchronous environments.
• An ownership mindset, with the ability to work independently and contribute effectively to shared codebases.

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