Machine Learning Engineer in Newbury

Working Hours: Full time 37.5 hours per week – Mon – Fri Hybrid

We believe that through collaboration and connection with our colleagues we can achieve great things. Our hybrid working approach allows our people to work both in the office and at home, providing the flexibility and resources you need to succeed in your role. We don't require you to be in on specific days; instead, we ask people to come into the office 2-3 days each week. You should work with your line manager to understand what their expectations are for you, your specific role and your team.

As ML Engineer you will be responsible for bridging the gap between data science experimentation and production‑grade, scalable ML systems. You will own the engineering excellence required to take validated models from data scientists and deploy them reliably across the organisation's fragmented platform estate. You will work across the full ML Operations lifecycle—designing deployment pipelines, implementing model serving infrastructure, establishing monitoring and governance frameworks, and automating retraining workflows. Your role is critical to standardising practices across platforms and ensuring models can be built, deployed, and maintained consistently regardless of underlying infrastructure differences. You will collaborate closely with Data Scientists on model productionisation, AI Engineers on platform infrastructure requirements, and Analytics Engineering on data pipeline dependencies and reliability.

• Design, build, and maintain ML deployment pipelines and model serving infrastructure for both real‑time and batch inference workloads across multiple platforms.
• Establish comprehensive model monitoring, alerting, and performance tracking systems in production environments to ensure reliability and early problem detection.
• Implement model versioning, reproducibility, and automated retraining workflows that enable fast iteration whilst maintaining stability.
• Partner with Data Scientists to productionise validated experimental models, translating research outputs into robust, maintainable systems.
• Contribute to platform standardisation efforts across the fragmented estate, identifying common patterns and opportunities for reuse.
• Design and implement CI/CD pipelines tailored for ML workloads, ensuring quality, traceability, repeatability.
• Support governance and compliance requirements through technical documentation, audit trails, and reproducible deployment processes.
• Monitor and optimise compute resource allocation and infrastructure costs across platforms, applying FinOps principles.
• Collaborate with Analytics Engineering to ensure data pipeline reliability, quality, and performance for ML workloads.
• Contribute to team knowledge sharing and best practice documentation across the ML Engineering function.

Qualifications Job Requirements, Knowledge & Experience

We are looking for someone passionate and dedicated about ensuring our ML solutions are scalable, secure and responsibly deployed.

• Proven experience in ML engineering or ML Operations roles with multiple production model deployments at scale.
• Strong Python programming skills with software engineering fundamentals: testing, version control, code quality, and design patterns.
• Hands‑on experience with ML platforms such as Azure AI Foundry, Azure ML, Databricks, GCP, or equivalent.
• Solid understanding of containerisation and orchestration technologies.
• Demonstrable experience designing and implementing CI/CD pipelines for machine learning workloads.

We are regulated by the Financial Conduct Authority and all offers of employment for this role are subject to background checks, including criminal (DBS) and financial checks to meet the regulators standards. We believe everyone should have the opportunity to interview for a role that matches their skills. In collaboration with our Talent, Diversity & Inclusion teams and our employee‑led DEI Networks, we identified a range of reasonable adjustments to help you feel comfortable and perform at your best self during the interview process. If you require any reasonable adjustments or have an accessibility request as part of your recruitment journey, for example, extended time or breaks in between online assessments, a sign language interpreter, or assistive technology, please contact your recruiter directly or email jobs@three.co.uk for guidance.