At a Glance
- Tasks: Drive production-grade ML services on Azure, ensuring reliability and performance.
- Company: Join the UK's largest online pharmacy with a commitment to excellence.
- Benefits: Enjoy competitive pay, flexible hours, and extensive health benefits.
- Other info: Dynamic team culture with opportunities for professional growth and social events.
- Why this job: Make a real impact in digital healthcare while working with cutting-edge technology.
- Qualifications: Strong Python skills and experience with ML frameworks required.
The predicted salary is between 50000 - 60000 £ per year.
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
- 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
Applicants must prove they have the right to live in the UK. All successful applicants will be required to undergo a DBS check.
Machine Learning Operations Engineer in Leeds employer: Pharmacy2U Ltd
Pharmacy2U Ltd is an exceptional employer located in Leeds, offering a dynamic work environment that prioritises patient safety and team collaboration. With competitive pay, private healthcare, and a robust pension scheme, employees are supported in their professional growth while contributing to high-quality care. The company fosters a culture of teamwork and continuous improvement, making it an ideal place for those seeking meaningful and rewarding employment in the healthcare sector.
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We think you need these skills to ace Machine Learning Operations Engineer in Leeds
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Pharmacy2U Ltd, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Pharmacy2U Ltd. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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✨Brush Up on Your Statistics
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
