Senior Machine Learning Engineer – Applied AI

CompanyWomen in Data®

LocationLondon Area, United Kingdom

Posted At3/6/2026

UK Visa Sponsorship Analytics

Occupation Type

Programmers and software development professionals

Occupation Code Skill LevelHigher Skilled

Sponsorship Salary Threshold

£54,700 (£28.05 per hour)

Occupation rate applies

Above analytics are generated algorithmically based on job titles and may not always be the same as the company's job classification. You can also check detailed occupation eligibility, and salary criteria on our UK Visa Eligible Occupations & Salary Thresholds page.

Disclaimer: Hunt UK Visa Sponsors aggregates job listings from publicly available sources, such as search engines, to assist with your job hunting. We do not claim affiliation with Women in Data®. For the most up-to-date job details, please visit the official website by clicking "Apply Now."

Description

Location: Hybrid / London or Peterborough

Team: 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 systems—making sure that our AI ambitions are not only possible, but production-ready.

This role blends hands-on engineering with architectural design, experimentation support, and MLOps best practices. You’ll work closely with data scientists, platform engineers, and product teams to build the infrastructure and tooling that powers our most advanced models. You’ll also contribute to technical standards, advocate for scalable and responsible ML development, and help shape a high-performance ML Engineering function.

What you’ll be doing

ML Engineering & Deployment

Own the end-to-end delivery of production ML 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, Airflow)

Contribute hands-on code to model packaging, deployment, and lifecycle automation

Build systems that monitor model performance, drift, and operational health in real time

Support both batch and real-time ML workloads depending on use case requirements

Platform & Standards

Define and promote best practices for reproducibility, testing, CI/CD, and model observability
Help evolve our internal ML platform to support experimentation, governance, and collaboration
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 architectural guidance across multiple ML projects and technical design sessions

Culture & Innovation

Contribute to a culture of engineering excellence, collaboration, and learning
Stay up to date on emerging tools and approaches in MLOps and applied AI
Support responsible AI practices by contributing to explainability, auditability, and fairness initiatives 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 (and optionally one other language, such as Go or Java)
Experience with modern ML tooling (e.g. MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI)
Familiarity with CI/CD pipelines and infrastructure-as-code (e.g. Terraform, CloudFormation, GitHub Actions)
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; able to work across disciplines

Nice to Have

Experience working in regulated sectors such as insurance, banking, or financial services

Familiarity with platforms such as Databricks, SageMaker, Vertex AI, or Kubeflow

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.

We are proud supporters of Women in Data®. Connect, engage and belong to the largest free female data community in the UK – visit: www.womenindata.co.uk to join our community.

Stay connected! Follow us on LinkedInfor updates on career opportunities and more.