Senior Machine Learning Engineer

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. The problems are genuinely hard: ambiguous inputs, high-stakes decisions, and the kind of domain complexity that makes for satisfying engineering.

We’re also fundamentally rethinking how we deliver software. We’re moving toward an agentic-first development model - using AI agents not just in what we build for the business, but in how we build it. The goal is to multiply engineering delivery by an order of magnitude: not by cutting corners or generating throwaway code, but by designing robust systems and processes that let agents handle well-defined work while engineers focus on architecture, design, and the problems that actually require human judgment. This is a deliberate, engineering-led approach. We care about code quality, testability, and maintainability - the agent-generated code meets the same standards as everything else. We expect that a successful candidate will be able to bring their expertise to help guide and refine our agentic development process as it matures - a meaningful opportunity to influence how we work as well as what we ship.

This is a Senior Machine Learning / AI Engineer role with genuine technical leadership scope. You’ll shape the architecture of AI-driven production microservices, own system design decisions, and work at the intersection of traditional software engineering and applied AI. You’ll collaborate closely with engineers, data scientists, and product managers in a team that’s small enough for your decisions to matter and ambitious enough for the work to stay interesting.

About the role

• 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.

About you

We’re looking for an experienced AI or Machine Learning Engineer with strong software engineering fundamentals and a passion for building robust, production-ready AI systems. You’ll be someone who enjoys solving complex technical problems, making thoughtful architectural decisions and working on systems where quality, reliability and maintainability really matter.

You’ll have:

• 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.