MLOps Engineer - Hybrid (GCP Vertex AI)

Morrisons; Tonedale; Hybrid; GBP 65,000 - 90,000; Full time

Benefits offered by this job

Store discount

Annual bonus

Pension scheme

Private Healthcare

Holiday entitlement

Free parking onsite

Career progression

Job summary

Morrisons is seeking a Mid-Level Data MLOps Engineer to operationalise and scale ML models in production. The role focuses on building and automating the infrastructure powering retail ML initiatives, from forecasting to loyalty engines, using GCP Vertex AI, Terraform, and robust CI/CD pipelines.

You will collaborate with the Senior Data Ops leader and Data teams to design, implement, and iterate ML Ops frameworks, supporting forecast models that drive tangible business value and performance

Qualifications

• 3–5 years of production MLOps/DevOps/ML engineering experience focused on productionising models.
• Professional Python programming with clean, modular, testable code.
• Vertex AI Pipelines and managing production ML lifecycles in a real-world setting.
• BigQuery querying and orchestrating complex Airflow workflows.
• Jenkins scripts and container/artefact lifecycles in registries.
• Terraform IaC for secure, reproducible GCP environments.
• Experience with unit testing (pytest) and CI pipelines integration.
• Model deployment lifecycle including preprocessing, performance optimization, and serialization (e.g., Pickle, ONNX, SavedModel).
• Familiarity with container registries (GCP Artifact Registry) and secure CI/CD pipelines.
• Experience with data platform operations and monitoring model/data drift.

Responsibilities

• Design, orchestrate, and maintain automated ML pipelines using Vertex AI ML Pipelines and Airflow.
• Deploy models and manage inference latency to keep production Retail models accurate and reliable.

Skills

Python

Vertex AI

Airflow

BigQuery

Terraform

Jenkins

Docker

GCP

CI/CD

Unit testing

Tools

Kubernetes

GKE

Job description

Morrisons is seeking a Mid-Level Data MLOps Engineer to operationalise and scale ML models in production. The role focuses on building and automating the infrastructure powering retail ML initiatives, from forecasting to loyalty engines, using GCP Vertex AI, Terraform, and robust CI/CD pipelines.

You will collaborate with the Senior Data Ops leader and Data teams to design, implement, and iterate ML Ops frameworks, supporting forecast models that drive tangible business value and performance