Role Overview
We 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 Structure
Model: Hybrid
- On-site Expectancy: 3 days per week at our head office.
Key Responsibilities
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.
In return for all your hard work, you will receive:
15% discount in store from the day you join us
Additional 10% discount card for a friend or family member
Annual bonus scheme
Career progression and development opportunities
Generous holiday entitlement
Market leading pension scheme and life assurance
Healthcare benefits including Aviva Digital GP
‘MyPerks’ giving you discount with over 850 retailers
Free parking onsite
Enhanced Family, Maternity and Paternity Leave
Private Healthcare
Alive with activity, our modern Head Office is home to our corporate teams that make sure everything runs smoothly. Here, you’ll find comfy breakout areas, a coffee shop, Morrisons Daily and a subsidised restaurant. We are within commuting distance of Leeds, Manchester and the Yorkshire Dales - and we even have free parking!
At our Head Office you will expect to find supplier showcases, charity fundraising and celebrations all year round for the events that mean the most to our colleagues.
There’s more to our business as it’s fast paced and ever changing, as such we’ve got lots of fresh opportunities for you to play your part in our success. We’d love to meet you!
At Morrisons, we’re proud to be building a team that reflects the diversity of the communities we serve. We want every colleague to feel respected, supported and able to be themselves at work. Different voices, experiences and ways of thinking help us grow and improve and that’s good for our customers too.
We’re always looking for people from all walks of life to join us and bring their talents to our team. Together, we can build a workplace where everyone has the chance to thrive, make a difference and belong.
Required Skills & Qualifications
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.
