ML Ops Engineer

Explore Group; Remote; GBP 80,000 - 100,000; Part time

Job summary

A leading organization in the iGaming sector is looking for a Lead ML Ops Specialist to drive the deployment and scaling of their Machine Learning initiatives. This senior, hands-on consulting role involves defining the ML platform, establishing pipelines, and coaching less experienced engineers. The ideal candidate will have expertise in AWS SageMaker, Snowflake, and various ML tools. This position offers the opportunity to work in a dynamic environment, influencing ML strategy across multiple brands.

Qualifications

• Experience leading ML Ops delivery at SFIA level six or seven.
• Proven track record in building and scaling end-to-end ML platforms.
• Strong ability to establish ML infrastructure, CI CD for ML.

Responsibilities

• Lead ML Ops delivery for the organization.
• Build and scale ML platform capability across brands.
• Establish ML infrastructure and orchestration workflows.

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