Senior Machine Learning Engineer
Hybrid London Full-time Senior Machine Learning Engineering
At a glance
Senior Machine Learning Engineering role at CFC. London · full-time · no salary stated · we estimate £125,500–£152,500.
Estimated pay
CFC has not published a salary for this role. The figure below is our estimate, not theirs.
£125,500 – £152,500
What the middle half of senior machine learning engineering roles in London paid, among postings that did state a salary.
Worked out from 7 postings that stated pay, median £128,000. Last updated Oct 9, 2026.
RoleMachine Learning Engineering
SenioritySenior
LocationLondon
WorkplaceHybrid
EmploymentFull-time
PostedJul 23, 2026 · 3mo ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Build and maintain production AI agents that use business requirements to guide multi-step decisions
- Create tests, evaluations and monitoring to check agent behavior and improve live services
- Lead system design choices for AI microservices, balancing technical needs with practical trade-offs
- Write scalable Python services, document their architecture and keep them reliable in production
- Review code and coach junior engineers while working with engineers, data scientists and product managers
What you bring
- Substantial experience building production AI agents or services powered by large language models
- Strong Python skills, plus significant experience writing and maintaining production software
- Experience operating business-critical live systems and deploying or monitoring ML or LLM models
- Knowledge of asynchronous programming, Docker and modern service architectures, including distributed systems
- Hands-on use of LangChain or a related framework to coordinate LLM workflows
Who this fits
This senior role suits an engineer ready to lead architecture decisions, guide junior colleagues and work across applied AI and software engineering. The work is hybrid in London.
From the employer
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.
You’ll have:
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.
- 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.
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.