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# Data & Analytics Specialist - MLOps Engineer

- **Company:** Morrisons
- **Location:** Wellington, UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Apache HTTP Server, Unit Testing, BigQuery, Cloud Computing, Static Program Analysis, Continuous Integration, Information Engineering, Data Retrieval, Data Warehousing, DevOps, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, Transaction Data, Google Cloud, Feature Engineering, Delivery Pipeline, Infrastructure as Code (IaC), Pytest, Containerization, Kubernetes, Deployment Automation, ONNX (Open Neural Network Exchange) Format, Data Analytics, Machine Learning Operations, Docker, Jenkins
- **Published:** October 27, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/data-analytics-specialist-mlops-engineer/53912799

## About the Role

Lifecycle: Own the model deployment process, including data pre-processing, performance optimisation, model serialisation (e.g., Pickle, ONNX, TensorFlow SavedModel), and automated continuous training.Infrastructure as Code: Provision and manage secure, reproducible GCP environments and ML infrastructure modules using Terraform.CI/CD & Container Management: Build and optimize automated deployment pipelines using Jenkins, managing containerisation workflows with Docker and implementing secure container and artefact registries (e.g., GCP Artifact Registry).Code Quality & Engineering Excellence: Enforce software engineering best practices within the data science lifecycle by implementing comprehensive unit test coverage and static code analysis into CI pipelines.Data Platform Operations: Interface with our core retail data warehouse, optimizing data retrieval from BigQuery and streaming/batch feature processing via & Alerting: Implement monitoring systems for model drift, data drift, and, static code analysis tooling.Soft SkillsAbility to collaborate equally well with data engineers engineers and data analysts.Ability to collaborate and influence Onshore and Offshore supplier engineering build teamsExcellent communication skills - can translate complex technical solutions into language non technical stakeholders can understand Clear documentation skills for infrastructure templates and pipeline logic.Preferred/Nice-to-HavesExperience in supermarket retail or e-commerce, processing high-volume transaction data for demand forecasting or building and tuning scalable feature engineering pipelines inside Google Cloud DataflowGoogle Cloud certifications (e.g., Professional Machine Learning Engineer or Professional Cloud DevOps Engineer).Familiarity with Kubernetes and Google Kubernetes Engine (GKE) for hosting custom inference endpoints. More about our perksIn return for all your hard work, you will receive:15% discount in store from the day you join us Additional 10% discount

## Description

More about the roleWe are looking for a Mid-Level Data MLOps Engineer to operationalise, scale, and maintain our machine learning models in production. In this role, you will build and automate the infrastructure that powers our retail ML initiatives, from demand forecasting and inventory optimization to personalized customer loyalty engines. You will manage the end-to-end ML lifecycle on Google Cloud Platform (GCP) using Vertex AI, robust Infrastructure as Code (IaC), and secure CI/CD pipelines.You work closely with our Senior Data Ops leader, our Data Science community and Data Engineering supplier, designing, building and iterating ML Ops frameworks. Supporting our forecasting data models that deliver tangible business value. Work StructureModel: HybridOn-site Expectancy: 3 days per week at our head office.Key ResponsibilitiesML Pipeline Automation: Design, orchestrate, and maintain automated machine learning pipelines using Vertex AI ML Pipelines and Apache Airflow.Model Deployment &, inference latency to ensure production retail models remain accurate and reliable.More about youRequired Skills & QualificationsExperience: 3 to 5 years of production experience in an MLOps, DevOps, or Machine Learning Engineering role focused on productionising models.Programming: Professional-level proficiency in Python, with a deep understanding of writing clean, modular, and testable code.ML Ops Stack: Professional experience implementing Vertex AI ML Pipelines and managing production machine learning lifecycles.Data Technologies: Practical experience querying BigQuery and orchestrating complex workflows with Apache Airflow.CI/CD & Registries: Solid experience configuring pipeline scripts in Jenkins and managing container/artefact lifecycles within image registries.Infrastructure as Code: Hands-on experience writing and maintaining infrastructure modules using Terraform.Quality Assurance: Direct experience establishing mandatory unit testing frameworks (e.g., pytest) and automated