Technology - ML Ops Engineer

Role: ML Ops Engineer

Location: We operate a hybrid schedule, meaning 2-3 days a week in the office based at Thorpe Park, Leeds.

Salary: £ DOE plus extensive benefits

Contract type: Permanent

Employment type: Full time

Working hours: We work on a core hours principle. Our core hours are 09:30 - 16:00; you can work around these to suit you!

Do you want to work for the nation’s largest online pharmacy ensuring excellence for all our patients? We’re a market leader in the pharmacy world, with 25 years’ experience, helping over 1.8 million patients in England manage their NHS prescriptions from request through to delivery. We are Great Place to Work certified as we consider colleague experience a top priority every day, and as a certified B Corp we also meet high standards of social and environmental responsibility. Our people are fundamental to our success and ensuring we achieve our vision to be a world leading, patient-centric digital healthcare provider. We are committed to continuing to develop a positive, open and honest working environment for all.

Our tech teams keep us running 24/7 to make sure all our patients get world class service. To support that, this role may include participation in an out-of-hours rota as required by the business. We operate fair scheduling process as well as additional compensation for all on call periods.

The ML Ops Engineer will drive the operation of production‑grade Machine Learning and LLM services on Azure, ensuring models run as reliable, scalable, and high‑performing systems. Owning the end‑to‑end MLOps/LLMOps lifecycle, the role leads on CI/CD, deployment automation, monitoring, and incident response.

Working closely with Data Science, this role turns models into robust production services, bringing strong governance, observability, and continuous optimisation to ensure fast, safe, and efficient delivery at scale.

Why you’ll love working with us

We believe great people deserve great support. That’s why we offer a benefits package designed to look after your health, finances, career and life outside work.

Financial security & rewards

Competitive contributory pension

Occupational sick pay

Long-service awards and refer-a-friend bonuses

Professional registration fees covered (GPhC, NMC, CIPD and more)

Cycle to Work and Green Car schemes (subject to eligibility)

Family-friendly

Enhanced maternity and paternity pay

Flexible hybrid working to help balance work and home life

Health & wellbeing

Private healthcare insurance at discounted rates (Aviva)

Employee Assistance Programme and in-house mental health support

Access to discounted gym memberships via Blue Light Card and benefits schemes

Regular health and wellbeing initiatives

Career growth

Strong commitment to CPD, training and professional development

Time off & flexibility

25 days’ annual leave, increasing with service

Buy and sell holiday scheme

Everyday perks & exclusive discounts

Blue Light Card and employee discount platform

Exclusive discounts at The Springs, Leeds

25% off health & beauty purchases

25% off Pharmacy2U Private Online Doctor services

Culture & community

Regular social events throughout the year

What you’ll be doing?

Production Deployment & Release Engineering

Design and operate CI/CD pipelines for ML models and LLM prompt‑flows, covering build, test, validation, deployment, and rollback

Own model registration and promotion across environments, ensuring traceability, governance, and auditability

Implement safe deployment strategies (e.g. blue/green, canary, champion/challenger)

Package and deploy containerised inference services and batch pipelines, ensuring repeatability and rapid rollback

Reliability Engineering (Day 2 Operations)

Run ML and LLM services as production‑grade systems, defining SLOs/SLIs, dashboards, and alerting

Lead incident response for runtime issues, including triage, mitigation, recovery, and post‑incident reviews

Develop and maintain operational runbooks covering restart, rollback, secret rotation, and safe‑mode scenarios

Improve service resilience and reduce MTTR through automation (e.g. self‑healing, retries, fallbacks, circuit breakers)

Observability (Service, Data, Model & Cost)

Implement monitoring for availability, latency, errors, resource usage, and job performance

Monitor data quality including freshness, volume, completeness, schema drift, and distribution changes

Monitor model performance, including drift and prediction distribution shifts, and track accuracy where labels exist

Instrument LLM services for token usage, latency, and safety signals, with clear visibility into cost, quotas, and risks

LLMOps: Lifecycle, Quality & Safety

Manage prompts and workflows as code, including versioning, code reviews, and automated regression testing

Own production configuration for LLM deployments, including model updates, limits, and safeguards

Partner with Data Science and Security to ensure robust safety practices, including PII protection and prompt‑injection testing

Security, Privacy & Governance

Implement secure access controls, identity management, and secrets handling aligned to best practice

Support production readiness through documentation, monitoring plans, cost models, and audit evidence

Ensure all changes follow structured governance, with clear traceability and reproducibility

Who are we looking for?

Strong Python engineering skills, with experience in ML frameworks such as scikit‑learn, PyTorch, or TensorFlow, and familiarity with experiment tracking

Comfortable working in regulated environments, with an understanding of privacy, auditability, change control, and handling sensitive data

Strong DevOps/SRE background, including CI/CD, Infrastructure as Code, monitoring and alerting, incident management, and reliability engineering

Hands‑on experience with containerisation using tools such as Docker and Kubernetes (e.g. AKS), including debugging, performance tuning, and working with container registries

Experience working with Azure, ideally including Azure Machine Learning (pipelines, registries, online and batch endpoints) and Azure Monitor or Log Analytics

Experience operationalising ML pipelines, including training, batch scoring, feature engineering workflows, and preventing training‑serving skew

Experience implementing safe deployment practices such as blue/green or canary releases, supported by automated validation

Understanding of data contracts, schema evolution, and data quality practices, with the ability to troubleshoot data drift and missing features

What happens next?

Please click apply and if we think you are a good match, we will be in touch to arrange an interview.

Applicants must prove they have the right to live in the UK.

All successful applicants will be required to undergo a DBS check.

Unsolicited agency applications will be treated as a gift.

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