Machine Learning Operations Engineer

Pharmacy2U Ltd

ML Ops Engineer ensuring reliable performance of Machine Learning services for a leading online pharmacy. Collaborating with Data Science to deliver robust production services in a hybrid working environment.

Posted 7/22/2026full-timeLeeds • 🇬🇧 United KingdomMid-LevelSenior💰 £NaN per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and operating CI/CD pipelines for ML models, ensuring robust deployment strategies and operational excellence. Proficient in Python engineering, containerization, and monitoring practices within regulated environments.

Highest-signal resume keywords

Python EngineeringCI/CD Pipeline DesignContainerization with Docker and KubernetesAzure Machine LearningDevOps/SRE Practices

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Hard Skills

ML Frameworks (Scikit-learn, PyTorch, TensorFlow)Experiment TrackingIncident ManagementMonitoring and AlertingData Quality PracticesSafe Deployment StrategiesOperationalising ML PipelinesFeature Engineering WorkflowsData ContractsSchema Evolution

Tools & Technologies

DockerKubernetesAzure MonitorLog AnalyticsCI/CD Tools

Industry Keywords

Regulated EnvironmentsPrivacyAuditabilityChange ControlSensitive Data Handling

Tech Stack

Tools & technologies

AzureDockerKubernetesPythonPyTorchTensorflow

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

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)
Enhanced maternity and paternity pay
Flexible hybrid working to help balance work and home life
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
Strong commitment to CPD, training and professional development
25 days’ annual leave, increasing with service
Buy and sell holiday scheme
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
Regular social events throughout the year