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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. These systems include real-time inference APIs, batch data products, and LLM-based agentic solutions. You will set technical direction, develop your team, and collaborate with various stakeholders 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, helping bring in 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 - we are interested in what you can do, not where you studied
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
Multi-step AI patterns
Experimentation at scale
Internal ML platforms
Experimentation frameworks
Feature stores
Responsible AI
Model governance

Location

London

Work Type

Hybrid

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 are 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.