MLOPs Engineer

Overview

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. 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

Responsibilities

• Design and deploy end-to-end MLOps processes, emphasizing governance and automation.
• Architect and implement real-time solutions for high-volume model serving.
• Lead integration and use of MLflow for model registry and deployment in Databricks.
• Build and automate CI/CD pipelines using GIT for model deployments.
• Optimize large-scale Spark/Python codebases for production efficiency.
• Act as the technical lead to embed MLOps standards in the Data Engineering team.

Requirements

• Proven experience in designing and implementing MLOps processes in production.
• Expert proficiency with Databricks and MLflow.
• Extensive engineering experience with Apache Spark and Python on large datasets.
• Strong experience with GIT for version control and CI/CD pipelines.
• Excellent SQL skills for data management.
• Familiarity with Google Cloud Platform (GCP) is a plus.
• Good understanding of data science fundamentals for optimization.