Data & Analytics Specialist - MLOps Engineer

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.

• 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