MLOPs Engineer

Harnham; Liverpool; Hybrid; GBP 80,000 - 100,000; Full time

Job summary

A leading e-commerce client in the UK is seeking an experienced MLOps Engineer to design and implement MLOps processes for real-time model serving. The ideal candidate has strong experience with Databricks, MLflow, and large datasets using Spark and Python. This role offers flexibility for remote work, with one day a week required in-office.

Qualifications

• Proven experience designing and implementing end‑to‑end MLOps processes in a production environment.
• Expert proficiency with Databricks and MLflow.
• Expert Apache Spark and Python engineering experience on large datasets.
• Strong experience with GIT for version control and building CI/CD/release pipelines.
• Excellent SQL skills.

Responsibilities

• Design and deploy end‑to‑end MLOps processes focusing on governance, reproducibility, and automation.
• Architect and implement solutions for real‑time model serving.
• Lead integration and use of MLflow within Databricks.
• Build and automate CI/CD pipelines using GIT.
• Profile and optimise large‑scale Spark / Python codebases.

Skills

MLOps

GIT

MLFlow

Spark

Python

SQL

GCP

DevOps

CICD

Job description

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:

• Design and deploy end‑to‑end MLOps processes, focusing heavily on governance, reproducibility, and automation.
• Architect and implement solutions to transition high‑volume model serving (10M+ customers, 1.2M+ product variants) to real‑time performance.
• Lead the optimal integration and use of MLflow for model registry, experiment tracking, and deployment within the Databricks platform.
• Build and automate robust CI/CD pipelines using GIT to ensure stable, reliable, and frequent model releases.
• Profile and optimise large‑scale Spark / Python codebases for production efficiency, focusing on minimising latency and cost.
• 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.

Nice-to-Have / Desirable 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.

Desired Skills and Experience

• MLOPS
• GIT
• MLFlow
• Spark
• Python
• SQL
• GCP
• DevOps
• CICD