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# Data & Analytics Specialist - MLOps Engineer

- **Company:** Morrisons
- **Location:** Bradford, UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Unit Testing, BigQuery, Static Program Analysis, Software Quality, Computer Programming, Continuous Integration, Information Engineering, Data Infrastructure, Data Retrieval, Data Systems, Data Warehousing, DevOps, Data Flow Control, 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, Terraform, Docker, Jenkins
- **Published:** September 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3da8dc1c18e98bc0

## About the Role

* Experience: 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 static code analysis tooling.

Soft Skills

 * Ability to collaborate equally well with data engineers engineers and data analysts.
 * Ability to collaborate and influence Onshore and Offshore supplier engineering build teams
 * Excellent 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-Haves

 * Experience in supermarket retail or e-commerce, processing high-volume transaction data for demand forecasting or personalization.
 * Experience building and tuning scalable feature engineering pipelines inside Google Cloud Dataflow
 * Google 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.

## Description

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., * ML Pipeline Automation: Design, orchestrate, and maintain automated machine learning pipelines using Vertex AI ML Pipelines and Apache Airflow.

 * Model Deployment & 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 Dataflow.
 * Monitoring & Alerting: Implement monitoring systems for model drift, data drift, and inference latency to ensure production retail models remain accurate and reliable.


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