Data & Analytics Specialist - MLOps Engineer
Summary
MLOps engineer at supermarket retailer Morrisons who operationalises and scales production ML models for forecasting, inventory optimisation, and loyalty. Day to day they own the end-to-end ML lifecycle on GCP (Vertex AI), building automated pipelines with Airflow, Terraform-based IaC, and secure CI/CD using Jenkins and Docker.
In this role you will operationalise, scale, and maintain production ML models for Morrisons’ retail initiatives. You will own end-to-end ML lifecycles on GCP with Vertex AI, IaC and secure CI/CD, collaborating with data teams and suppliers. You’ll build automated pipelines and deploy scalable ML solutions for forecasting, inventory optimization, and loyalty applications. This is a hands-on, cross-functional position that shapes how data drives business value at scale within a fast-paced retail environment. You’ll join a supportive team that values engineering excellence and measurable impact.
Pay / Benefits- 15% in-store discount from day one
- Annual bonus scheme
- Pension scheme
- Private healthcare
- Healthcare benefits (Aviva Digital GP)
- Career progression opportunities
- Design, orchestrate and maintain automated ML pipelines using Vertex AI ML Pipelines and Apache Airflow
- Own the model deployment lifecycle including data preprocessing, performance optimization, serialization, and automated retraining
- Provision and manage secure, reproducible GCP environments and ML infra modules with Terraform
- Build and optimize CI/CD pipelines and container workflows using Jenkins, Docker, and manage registries (GCP Artifact Registry)
- Enforce software engineering best practices with unit tests and static analysis in CI pipelines
- Interface with core data warehouse (BigQuery) and data processing (Dataflow) for feature engineering and retrieval
- Implement monitoring for model/data drift and inference latency in production
- Collaborate with data engineers, data scientists, and supplier engineering teams to design and iterate on ML Ops frameworks
- 3–5 years in MLOps, DevOps, or ML engineering focused on productionising models
- Python expertise with modular, testable code
- Experience with Vertex AI ML Pipelines and production ML lifecycles
- BigQuery querying and Airflow workflow orchestration
- Jenkins scripting and container/artefact registry management
- Terraform IaC experience
- Unit testing (pytest) and automated static analysis
- Excellent cross-functional collaboration and communication skills
- Documentation of infrastructure and pipeline logic
- collaboration with data engineers and analysts
- ability to influence supplier build teams
- clear communication with non-technical stakeholders
- Vertex AI ML Pipelines
- Apache Airflow
- Terraform