Accepting applications until:

10 July 2026

Job Description

We are Global

We’re proud to be one of the world’s leading media and entertainment groups. Whether it be on-air, via global player or through our outdoor advertising, we entertain and reach over 50 million individuals across the UK every week.

Across our entire business, we’re committed to making more moments that matter for our audiences, customers and for each other. And every moment matters…the small, the big and everything in between. We couldn’t do any of it without our talented, passionate Globallers. Everything we do is driven by our culture and the talented people who make it happen.

Here at Global, we have a saying…it’s all about how you make people feel. It’s our company ethos, our guiding belief and it’s so much more than words. It’s the vibe you get when you walk into one of our offices, it’s what keeps us honest and true to who we are, and above all, it’s the reason we all love to work here.

Description

Senior MLOps Engineer

Overview of job

This role is part of our Global:IQ team, the group developing our new intelligence platform. Global:IQ brings together a suite of 1st party and partner data, tools and capabilities to turn data into audience understanding and optimised, >3 best things about the job

• Build from Zero: You're not maintaining legacy systems—you're establishing the MLOps patterns, tooling and standards that will scale with the team for years to come.

• AI at the Core: This is a true AI/Data-driven product. ML isn't a nice-to-have feature - it's the product. Your infrastructure directly enables business value.

• Truly Cross-functional: the Global:IQ team is a tight collaboration between technical and commercial areas.

Measures of success

In the first few months, you would have:

• Defined a clear operating model between Data Engineering/MLOps and teams responsible for model development.

• Onboarded key 1st and 3rd party datasets following existing ingestion patterns/standards.

• Delivered an initial end-to-end MLOps path for at least one production ML use case, from model handoff through deployment, monitoring and rollback.

• Established baseline operational standards including model versioning, environment management, deployment patterns and handover processes between Data Science and Engineering.

• Implemented monitoring and alerting for production ML workloads, covering operational health, data quality and model performance signals.

• Defined a clear operating model and interfaces between teams developing models and teams operating them in production.

• Built collaborative relationships with Data Science, Data Engineering and Product stakeholders, demonstrating pragmatic judgement and delivery pace.

Key Responsibilities of the Role

ML Infrastructure & Deployment (40%)

• Design, build and maintain automated pipelines for model training, validation, packaging and deployment across development, staging and production environments.

• Implement model registries, experiment tracking and versioning systems to ensure reproducibility and traceability.

• Build and operationalise batch, streaming and near-real-time inference services depending on product requirements.

• Create reusable patterns and self-serve tooling that enable Data Science teams to deploy models independently while adhering to operational standards.

• Implement infrastructure for feature engineering pipelines, feature stores, and consistent serving layers between training and inference.

Model Monitoring & Operations (30%)

• Implement comprehensive monitoring for ML workloads including prediction latency, throughput, error rates, input data quality and feature drift.

• Build alerting and automated recovery mechanisms to ensure SLAs for ML services are met.

• Establish processes for model rollback, rollout strategies (canary, blue-green) and incident response.

• Define and track operational KPIs for ML systems and lead post-incident reviews to drive continuous improvement.

• Work with Data Science teams to implement model performance tracking, drift detection and retraining triggers.

MLOps Governance & Best Practice (20%)

• Establish governance controls for model lineage, approval workflows, reproducibility and audit trails.

• Define and enforce standards for model packaging, environment management, dependency management and code quality for ML workloads.

• Introduce CI/CD patterns specific to ML including automated testing (unit, integration, model validation), promotion gates and release automation.

• Document MLOps processes, runbooks and architectural decisions, enabling knowledge sharing across the team.

• Stay current with industry trends and tooling, evaluating and adopting fit-for-purpose technologies as the platform matures.

Collaboration & Enablement (10%)

• Partner closely with Data Scientists and ML Engineers to understand requirements and translate experimental work into production-ready systems.

• Work with Data Engineering teams to ensure data pipelines, governance and quality controls support ML use cases effectively.

• Support and mentor junior engineers, raising the bar on MLOps practices and operational excellence across the team.

• Communicate clearly with non-technical stakeholders about ML system health, risks and tradeoffs.

What you will need

The ideal candidate will be pragmatic, hands-on, and passionate about making ML systems reliable, scalable and maintainable in production.

Essential Skills & Experience:

• Strong programming skills (Python preferred) with a focus on production-quality, testable and maintainable code.

• Hands-on MLOps experience: You have operationalised ML models in production, owning deployment, monitoring and lifecycle management (not just experimentation).

• Cloud platform expertise (AWS strongly preferred; Snowflake a plus) with deep understanding of services for compute, orchestration, storage and ML (e.g., SageMaker, Lambda, ECS/EKS, Step Functions).

• Experience with MLOps tooling such as:

  • Experiment tracking and model registries (MLflow, Weights & Biases, SageMaker Model Registry)

  • Workflow orchestration (Airflow, Prefect, Step Functions)

  • Model serving frameworks (SageMaker, TorchServe, TensorFlow Serving, or similar)

  • Feature stores (Feast, Tecton, or custom-built)

• Deep understanding of monitoring and observability for ML systems, including operational metrics, data quality checks, drift detection and model performance tracking.

• CI/CD and Infrastructure as Code: Experience with ML-specific CI/CD patterns, Terraform, containerisation (Docker), and testing automation for ML pipelines.

• Practical experience building MLOps from an early stage, including sensible tool selection, pattern definition and iterative delivery.

• Ability to work across disciplines: You can translate between Data Science language and Engineering standards, establishing clear contracts and interfaces.

• Strong communication skills: You can explain technical decisions, trade-offs and system behaviour to both technical and non-technical audiences.

• Analytical and >Everyone is welcome at Global

  Just like our media and entertainment platforms are for everyone, so are our workplaces. We know that we can’t possibly serve our diverse audiences without first nurturing and celebrating it in our people and that’s why we work hard to create an inclusive culture for everyone. We believe that different will set us apart, so no matter what you look like, where you come from or what your favourite radio station is, we want to hear from you.