Pharmacy2U
Healthcare
Technology-MLOpsEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Technology - ML Ops Engineer at Pharmacy2U. Skills: ML Ops, LLMOps, Azure, CI/CD. Drive operation of ML/LLM services. Own end-to-end MLOps/LLMOps lifecycle”
What You'll Achieve.
ensure models run as reliable, scalable, and high-performing systems; ensure fast, safe, and efficient delivery at scale; reduce MTTR through automation
Industry & Context.
troubleshoot data drift; missing features
participation in an out-of-hours rota, additional compensation for on call periods, right to live in the UK, DBS check
What They're Looking For.
Must Have
Python engineering skills, experience in ML frameworks, familiarity with experiment tracking, working in regulated environments, understanding of privacy, auditability, change control, handling sensitive data, DevOps/SRE background, CI/CD, Infrastructure as Code, monitoring and alerting, incident management, reliability engineering, Hands-on experience with containerisation, Docker, Kubernetes, debugging, performance tuning, working with container registries, Experience working with Azure, Azure Machine Learning, Azure Monitor, Log Analytics, Experience operationalising ML pipelines, training, batch scoring, feature engineering workflows, preventing training-serving skew, Experience implementing safe deployment practices, automated validation, Understanding of data contracts, schema evolution, data quality practices, troubleshoot data drift, missing features, right to live in the UK, DBS check
Nice to Have
scikit-learn, PyTorch, TensorFlow, AKS, pipelines, registries, online and batch endpoints, blue/green or canary releases
What You'll Do.
Drive operation of ML/LLM services
Own end-to-end MLOps/LLMOps lifecycle
Lead deployment automation
Lead incident response
Turn models into production services
Ensure continuous optimisation
Design CI/CD pipelines
Operate CI/CD pipelines
Own model registration
Implement safe deployment strategies
Package inference services
Deploy batch pipelines
Lead incident response
Develop operational runbooks
Maintain operational runbooks
Improve service resilience
Monitor model performance
Instrument LLM services
Own production configuration
Partner on safety practices
Implement secure access controls
Implement identity management
Implement secrets handling
Support production readiness
Ensure changes follow governance
How You'll Work.
Team & Collaboration
Working closely with Data Science; Partner with Data Science; Partner with Security
Full Job Description
**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’
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