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About the Role
As a Machine Learning Engineering Manager in our Data & AI Solutions team, you will lead a team building and owning production systems at the heart of our AI platform. This includes real-time inference APIs, batch data products, and LLM-based agentic solutions. You will set technical direction, develop your team, and collaborate with colleagues to ensure the intelligence your team builds has a real effect on the business.
Responsibilities
- Lead and develop a team of ML engineers, creating the conditions for them to deliver robust, scalable systems into production - spanning real-time inference APIs, batch data products, and LLM-based agentic solutions
- Collaborate closely with data scientists to move prototypes into high-quality production systems, maintaining quality and performance as complexity scales
- Drive team planning, estimation, and sprint delivery - ensuring projects are delivered on time and to a high standard
- Ensure every system your team ships connects to a real outcome and has a mechanism to improve over time
- Develop your team of machine learning engineers through regular feedback, technical mentorship and honest career conversations
- Contribute to hiring, bringing together a group of diverse people who combine technical rigour with curiosity and commercial awareness
- Foster a team environment where ambition and delivery reinforce each other
- Contribute to standards and practices across the wider Data & AI Solutions chapter
- Set the standard for ML and AI engineering quality, reproducibility, and production-readiness across your team - covering model pipelines, deployment tooling, system design, and lifecycle automation
- Take ownership of how the team approaches production health: knowing when a system is degrading, having a plan to address it, and ensuring monitoring and observability are built in from the start
- Contribute to the design and evolution of our ML and AI tooling and shared platform components
- Be hands-on in design sessions, code reviews, and architectural decisions where your input matters
- Guide your team through a fast-moving tooling landscape spanning classical ML, LLM-based systems, and agentic AI patterns - knowing when each is the right approach
- Champion responsible, auditable AI - particularly important in a regulated financial services environment where precision and explainability are non-negotiable
- Encourage and model the use of AI-assisted development tools within your team, and be actively curious about how automated coding and workflow tools can increase the pace and quality of your team's output
- Work closely with data, product, commercial and engineering leads to translate strategic priorities into well-scoped work
- Represent your team's work and capability to senior stakeholders, building confidence in what the team delivers and how it operates
- Help shape platform direction by feeding back requirements from applied ML delivery
Requirements
- Experience leading engineering teams focused on machine learning, ML platforms and AI systems
- Proven track record deploying and managing ML and AI systems in production at scale - including real-time inference, batch data products, and LLM-based solutions
- Strong Python and ML and AI engineering fundamentals - sufficient to assess your team's work and contribute directly when needed
- Understanding of infrastructure-as-code and CI/CD for ML systems (e.g. Terraform, GitHub Actions, ArgoCD)
- Clear communication: able to make technical work legible to commercial and product audiences
- Experience working in agile environments with clear accountability to delivery
- A degree in a quantitative discipline, or equivalent experience with production ML and AI systems
- Hands-on experience with LLM-based systems: prompt engineering, RAG, tool use, or orchestration frameworks such as LangGraph or LangChain
- Familiarity with multi-step AI patterns - building systems where models plan, retrieve information, and take sequences of actions
- Understanding of experimentation at scale and the infrastructure needed to run it well
- Experience building or managing internal ML platforms, experimentation frameworks, or feature stores
- Interest in responsible AI and model governance practices
Skills
- Machine Learning
- AI Systems
- Python
- Infrastructure-as-code
- CI/CD for ML systems
- Terraform
- GitHub Actions
- ArgoCD
- Prompt engineering
- RAG
- Tool use
- Orchestration frameworks
- LangGraph
- LangChain
- Responsible AI
- Model governance
Location
- Hybrid, London office
Work Type
- Hybrid
- Full-time
Experience Level
- Manager
Education Level
- Degree in a quantitative discipline, or equivalent experience
About the Company
- Compare the Market is a purpose-driven business powered by tech and AI.
- We’re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace.
- Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers.
- We’ve carved a meerkat-shaped niche and we’re looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment.
- Compare the Market is at an inflection point. We are shifting from a business that answers data questions to one that builds the intelligence powering our AI systems and platform.
- We’re a business built for pace and performance. Here, you’ll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress.
- We believe diverse teams make better decisions, and we’re committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.
Equal Opportunity
- We believe diverse teams make better decisions, and we’re committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.