At a Glance
- Tasks: Drive production-grade ML services on Azure and ensure high performance.
- Company: Join the UK's largest online pharmacy with a focus on patient care.
- Benefits: Enjoy competitive pay, flexible hours, and extensive health perks.
- Other info: Dynamic team culture with great career growth opportunities.
- 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
- 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
- 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)
- 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
- 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
- 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
- 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 you’ll be doing?
Production Deployment & Release Engineering
Reliability Engineering (Day 2 Operations)
Observability (Service, Data, Model & Cost)
LLMOps: Lifecycle, Quality & Safety
Security, Privacy & Governance
Who are we looking for?
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.
Technology - ML Ops Engineer in Leeds employer: Pharmacy2U Ltd
Join a leading digital healthcare provider that prioritises colleague experience and offers a supportive work culture. With a commitment to professional development, flexible working arrangements, and extensive health and wellbeing benefits, you'll thrive in an environment that values your contributions while making a meaningful impact on patient care. Located in the vibrant Thorpe Park area of Leeds, you will enjoy a dynamic workplace with opportunities for social engagement and community involvement.
StudySmarter Expert Advice🤫
We think this is how you could land Technology - ML Ops Engineer in Leeds
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Prepare for those interviews! Research the company, understand their products, and be ready to discuss how your skills align with their needs. Practise common interview questions and have your own questions ready to show your interest.
✨Tip Number 3
Showcase your projects! If you've worked on any ML Ops projects, make sure to highlight them during interviews. Bring along examples of your work or even a portfolio to demonstrate your hands-on experience.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at the forefront of digital healthcare.
We think you need these skills to ace Technology - ML Ops Engineer in Leeds
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the ML Ops Engineer role. Highlight your Python skills, experience with ML frameworks, and any relevant DevOps background. We want to see how your experience aligns with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about this role and how you can contribute to our mission. Be genuine and let your personality come through – we love that!
Showcase Relevant Projects:If you've worked on any projects related to MLOps or have experience with Azure, make sure to mention them. We want to see your hands-on experience and how you've tackled challenges in the past.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at Pharmacy2U Ltd
✨Know Your Tech Inside Out
Make sure you brush up on your Python skills and get familiar with ML frameworks like scikit-learn, PyTorch, or TensorFlow. Be ready to discuss your hands-on experience with Azure and containerisation tools like Docker and Kubernetes, as these are crucial for the ML Ops Engineer role.
✨Understand the Role of CI/CD
Since this position involves designing and operating CI/CD pipelines, be prepared to explain your approach to deployment automation and incident response. Think about examples from your past work where you successfully implemented safe deployment strategies like blue/green or canary releases.
✨Showcase Your Problem-Solving Skills
The interviewers will want to see how you handle real-world challenges. Prepare to discuss specific incidents where you led incident response for runtime issues, including triage and recovery. Highlight your ability to improve service resilience through automation.
✨Emphasise Collaboration
This role requires close collaboration with Data Science and Security teams. Be ready to talk about how you've worked in cross-functional teams before, especially in regulated environments. Show that you understand the importance of governance and safety practices in ML operations.
