MLOPs Engineer

MLOps Engineer

Outside IR35 - 500-600 Per Day

Ideally, 1 day per week/fortnight in the office, flexibility for remote work for the right candidate.

A market-leading global e-commerce client is urgently seeking a Senior MLOps Lead to establish and drive operational excellence within their largest, most established data function (60+ engineers). This is a mission-critical role focused on scaling their core on-site advertising platform from daily batch processing to real-time capability.

This role suits a hands-on MLOps expert who is capable of implementing new standards, automating deployment lifecycles, and mentoring a large engineering team on best practices.

What you'll be doing:

MLOps Strategy & Implementation: Design and deploy end-to-end MLOps processes, focusing heavily on governance, reproducibility, and automation.

Real-Time Pipeline Build: Architect and implement solutions to transition high-volume model serving (10M+ customers, 1.2M+ product variants) to real-time performance.

MLflow & Databricks Mastery: Lead the optimal integration and use of MLflow for model registry, experiment tracking, and deployment within the Databricks platform.

DevOps for ML: Build and automate robust CI/CD pipelines using GIT to ensure stable, reliable, and frequent model releases.

Performance Engineering: Profile and optimise large-scale Spark/Python codebases for production efficiency, focusing on minimising latency and cost.

Knowledge Transfer: Act as the technical lead to embed MLOps standards into the core Data Engineering team.

Key Skills:

Must Have:

• MLOps: Proven experience designing and implementing end-to-end MLOps processes in a production environment.

• Cloud ML Stack: Expert proficiency with Databricks and MLflow.

• Big Data/Coding: Expert Apache Spark and Python engineering experience on large datasets.

• Core Engineering: Strong experience with GIT for version control and building CI/CD / release pipelines.

• Data Fundamentals: Excellent SQL skills.

• DevOps/CICD (Pipeline experience)

• GCP (Familiarity with Google Cloud Platform)

• Data Science (Good understanding of math/model fundamentals for optimisation)

• Familiarity with low-latency data stores (e.g., CosmosDB).

If you have the capability to bring MLOps maturity to a traditional Engineering team using the MLFlow/Databricks/Spark stack, please email: with your CV and contract details.