MLOps Engineer

Overview

In this role you will drive the MLOps strategy and hands-on execution to scale a large on-site advertising platform from batch to real-time. You’ll lead governance, reproducibility, and automation within a 60+ engineers data function, mentoring the team to adopt best practices. The position centers on Databricks/MLflow-based pipelines, CI/CD for ML, and performance optimization of large Spark/Python workloads. You’ll work in a global, data-driven environment with a mission to accelerate operational excellence.

Pay / Benefits

• remote work flexibility
• in-office 1 day per week
• highly competitive day rate
• outside IR35
• global leading employer in data & analytics recruitment

Responsibilities

• Design and implement end-to-end MLOps processes emphasizing governance, reproducibility, and automation
• Architect and deploy real-time data pipelines for high-volume model serving
• Lead integration and usage of MLflow for registry and experiment tracking within Databricks
• Build and automate CI/CD pipelines for reliable model releases
• Profile and optimise large-scale Spark/Python codebases for production efficiency
• Act as technical lead to embed MLOps standards into the core Data Engineering team

Key requirements

• MLOps experience delivering end-to-end production-grade processes
• Databricks and MLflow expertise
• Apache Spark and Python engineering on large datasets
• Strong GIT experience and CI/CD/release pipeline building
• Excellent SQL skills
• familiarity with cloud platforms (GCP preferred) and DevOps for ML (nice-to-have)

• mentoring a large engineering team
• leadership and collaboration
• stakeholder communication
• MLflow
• Databricks
• Apache Spark

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