Data & Analytics Specialist - MLOps Engineer

More about the role

We are looking for a Mid-Level Data MLOps Engineer to ope rationalise, 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, Tensor Flow Saved Model), 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 Big Query 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.

More about you Required

Skills & Qualifications

Experience:

3 to 5 years of production experience in an MLOps, Dev Ops, or Machine Learning Engineering role focused on product ionising 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 life cycles.

Data Technologies:
Practical experience querying Big Query and orchestrating complex workflows with Apache Airflow.

CI/CD & Registries:
Solid experience configuring pipeline scripts in Jenkins and managing container/artefact life cycles 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.,…