ML Ops Engineer

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

Job summary

A leading tech organization is seeking a Lead ML Ops Specialist to drive the development of an AI and Automation platform. In this hands-on consulting role, you will lead ML Ops delivery, establish ML infrastructure, and coach less experienced engineers. The ideal candidate will thrive in ambiguity and possess strong technical skills in AWS SageMaker, Snowflake, and GitLab. This role offers the opportunity to shape the future of their AI strategy across multiple brands in a dynamic environment.

Qualifications

• Proficient in AWS SageMaker for deploying machine learning models.
• Experience with Snowflake for data warehousing and analytics.
• Strong familiarity with version control systems like GitLab.

Responsibilities

• Lead ML Ops delivery at SFIA level six or seven.
• Build and scale end to end ML platform capability.
• Establish ML infrastructure and CI/CD for ML processes.

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