Job Description
Senior Machine Learning Engineer – Applied AI
Location: Hybrid / London or PeterboroughTeam: Data & AI
Why this role matters
At Compare the Market, we’re scaling our AI capabilities to power intelligent, personalised experiences that help millions make smarter financial decisions. As a Senior Machine Learning Engineer, you’ll play a critical role in enabling the deployment, monitoring, and scaling of production‑grade ML and AI systems—making sure that our AI ambitions are not only possible, but production‑ready.
What you’ll be doing
ML Engineering & AI Systems
- Own the end‑to‑end delivery of production machine learning and AI solutions in collaboration with data scientists and product teams
- Design and build model pipelines for training, validation, and deployment using modern tooling (e.g. MLflow, Kubernetes)
- Contribute hands‑on code to model packaging, deployment, and lifecycle automation
- Build systems that monitor model performance, drift, reliability and operational health in real time
- Support both batch and real‑time ML workloads depending on use case requirements
- Work on emerging AI and LLM‑powered capabilities, helping integrate modern AI techniques into production systems where they can deliver real user value
Platform & Standards
- Help evolve our internal ML and AI platform to support experimentation, governance, and collaboration
- Define and promote best practices for ML & AI system design, including reproducibility, testing, CI/CD, model & agent observability and evaluation
- Develop shared tools and libraries that accelerate safe, efficient, and scalable ML development
Collaboration & Technical Leadership
- Work closely with data scientists to productionise experimental models and turn prototypes into robust services
- Act as a technical mentor and code reviewer for other engineers and contributors
- Provide technical leadership across ML and AI initiatives, contributing to architecture discussions and design reviews
Culture & Innovation
- Contribute to a culture of engineering excellence, collaboration, and continuous learning
- Stay up to date on emerging tools and approaches in MLOps and applied AI, helping evaluate and adopt technologies where appropriate
- Support responsible AI practices, contributing to explainability, auditability, and fairness in ML systems
What we’re looking for
- Strong experience deploying ML models into production in cloud‑native environments
- Solid software engineering skills in Python, with experience building scalable services, APIs, and production‑quality code
- Experience with modern ML tooling and platforms (e.g. Databricks, MLflow, Airflow, Kubeflow, SageMaker, Vertex AI)
- Familiarity with CI/CD pipelines and infrastructure‑as‑code (e.g. Terraform, CloudFormation)
- Experience building robust, maintainable, and testable ML pipelines and APIs, including batch or real‑time model delivery
- Strong understanding of ML lifecycle challenges—versioning, testing, monitoring, governance
- Excellent collaboration and communication skills, with experience working across data science, engineering, and product teams
Nice to Have
- Experience working in regulated sectors such as insurance, banking, or financial services
- Experience deploying real‑time or streaming ML models (e.g. Kafka, Flink, Spark Structured Streaming)
- Exposure to large language models (LLMs), vector databases, or RAG architectures
- Passion for automation, tooling, and building reusable systems
- Interest in responsible AI and ML model governance
Why Join Us
You’ll be joining a modern, fast‑growing ML Engineering team that’s powering real‑world AI at scale. With the right tools, support, and technical autonomy, you’ll help shape how we turn experimental models into trusted systems that deliver impact across our platform.
Everyone Is Welcome
We’re committed to building a diverse and inclusive Data & AI team where everyone feels they belong. If this role excites you but you don’t meet every single requirement, we still encourage you to apply. We care about what you can do—not just where you’ve been.
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