Ml Ops Engineer

Global; Greater London; On-site; GBP 85,000 - 130,000; Full time

Job summary

Global in Greater London is seeking an experienced MLOps Engineer to build and operate the AI production platform. You will own pipelines, model registries, monitoring, and governance to enable Data Science teams to deploy and retrain models at scale.

You'll collaborate with Data Science, Data Engineering and Product, implement CI/CD for ML, and mentor junior engineers to raise operational standards. This role sits in Global:IQ's AI infrastructure team in Greater London.

Qualifications

• Operationalised ML models in production with deployment, monitoring and lifecycle management.
• Strong Python programming for production-grade code.
• Deep AWS knowledge including SageMaker, Lambda, ECS/EKS and Step Functions.
• Experience with ML tooling: experiment tracking, registries, workflow orchestration and feature stores.
• CI/CD and IaC for ML: Terraform, Docker and test automation.
• Excellent cross-disciplinary communication translating DS needs to engineering.

Responsibilities

• ML Infrastructure & Deployment: build automated pipelines and model registries.
• Model Monitoring & Operations: implement monitoring, alerting and auto-recovery.
• Governance & Best Practice: establish lineage, reproducibility and audit trails.
• Collaboration & Enablement: mentor engineers and partner with cross-functional teams.

Skills

MLOps experience

Python

AWS SageMaker

Terraform

Docker

Cross-disciplinary communication

Tools

SageMaker

Lambda

ECS/EKS

Step Functions

Snowflake

Docker

Terraform

Job description

This job is with Global, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

Accepting applications until:

16 October 2026

Job Description

Your New Role

Global:IQ is the team building our new intelligence platform, turning first-party and partner data into smarter, data-led media plans across Global's audio and Outdoor inventory.

As a MLOps Engineer at Global, you'll build the operational infrastructure that brings AI and ML models into production. You'll own the platforms, pipelines and processes that let our Data Science teams deploy, monitor, retrain and govern models reliably at scale-from the ground up.

Key Responsibilities

• ML Infrastructure & Deployment (40%): Build automated pipelines for model training, validation and deployment, plus model registries, feature stores and inference services, with self-serve tooling for Data Science teams.
• Model Monitoring & Operations (30%): Implement monitoring, alerting and automated recovery for ML workloads-covering latency, data quality and drift-and own rollback, rollout and incident response.
• MLOps Governance & Best Practice (20%): Establish controls for model lineage, reproducibility and audit trails, and introduce ML-specific CI/CD, testing and release automation.
• Collaboration & Enablement (10%): Partner with Data Science, Data Engineering and Product, and mentor junior engineers to raise operational standards.

What you will love about this role:

• Think Big: This is a true AI-driven product-ML isn't a feature, it's the product, and your infrastructure directly enables business value.
• Own It: You're not maintaining legacy systems-you're establishing the MLOps patterns and standards that will scale for years.
• Keep it Simple: You'll build pragmatic, reusable patterns that keep ML systems reliable and maintainable without over-engineering.
• Better Together: Global:IQ is a tight collaboration between technical and commercial teams.

What Success Looks Like

In your first few months, you'll have:

• Defined a clear operating model between MLOps and the teams developing models.
• Delivered an end-to-end MLOps path for at least one production use case, from model handoff through deployment, monitoring and rollback.
• Established baseline standards for model versioning, environment management and deployment.
• Implemented monitoring and alerting across operational health, data quality and model performance.

What You'll Need

• MLOps experience: You've operationalised ML models in production, owning deployment, monitoring and lifecycle management.
• Strong programming: Production-quality, testable Python.
• Cloud expertise: Deep AWS knowledge (SageMaker, Lambda, ECS/EKS, Step Functions); Snowflake a plus.
• MLOps tooling: Experiment tracking and registries, workflow orchestration, model serving and feature stores.
• CI/CD & IaC: ML-specific CI/CD, Terraform, Docker and test automation.
• Cross-disciplinary communication: You translate between Data Science and Engineering and explain trade-offs to any audience.

ML Ops Engineer • Greater London, Greater London, United Kingdom