ML Ops Engineer

Explore Group; Remote; GBP 70,000 - 90,000; Full time

Job summary

A leading tech organization in the UK is seeking a Lead ML Ops Specialist to drive the deployment and scaling of Machine Learning across their platforms. This senior role entails working hands-on with the AI Ops team to define and establish ML infrastructure, create pipelines, and mentor junior engineers. The ideal candidate will excel in ambiguous environments and possess strong communication skills to convey ML concepts to a diverse audience. A competitive contract opportunity is available, allowing for UK remote work.

Qualifications

• Experience leading ML Ops delivery at SFIA level six or seven.
• Ability to build and scale end to end ML platform capability.
• Experience establishing ML infrastructure and orchestration workflows.

Responsibilities

• Lead ML Ops delivery at SFIA level six or seven.
• Build and scale end to end ML platform capability.
• Coach teams and guide decisions in an ambiguous environment.

Skills

AWS SageMaker

Snowflake

GitLab

MLflow

AWS Bedrock

Job description

Lead ML Ops Specialist

UK Remote • Contract • Outside IR35

I’m supporting an organisation in the iGaming space as they build and launch a completely new AI and Automation platform across their UK and International estate. They operate multiple brands across two platforms and are now integrating everything under one AI driven strategy — and they need a Lead ML Ops specialist to help drive it.

This is a senior, hands on consulting role shaping how Machine Learning is deployed, scaled and monitored across the business. You’ll work directly with their AI Ops team to define the platform, establish the pipelines, and coach less experienced engineers on best practice.

The Role

• Lead ML Ops delivery at SFIA level six or seven
• Build and scale end to end ML platform capability
• Establish ML infrastructure, CI CD for ML, and orchestration workflows
• Operate across a complex multi brand estate with high stakeholder visibility
• Coach teams, guide decisions, and bring structure to an ambiguous environment

Key Skills

• AWS SageMaker (essential)
• Snowflake
• GitLab
• MLflow
• AWS Bedrock (bonus)

Soft Skills

• Thrives in ambiguity
• Able to navigate political landscapes and conflicting priorities
• Strong communicator with the ability to explain ML concepts to non-specialists
• Coaching mindset