Your New Role
Senior MLOps Engineer
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 Senior 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.
Skills Required
- Strong programming skills and writing production-quality, testable, maintainable code
- Python (preferred)
- Hands-on MLOps experience deploying, monitoring and managing ML models in production
- Cloud platform expertise (AWS strongly preferred; experience with compute, orchestration, storage, ML services)
- Experience with MLOps tooling (e.g., MLflow, Weights & Biases, SageMaker Model Registry)
- Workflow orchestration experience (Airflow, Prefect, or Step Functions)
- Model serving experience (SageMaker, TorchServe, TensorFlow Serving or similar)
- Feature store experience or ability to implement feature engineering pipelines (Feast, Tecton, or custom)
- Monitoring and observability for ML systems including data quality, drift detection and model performance tracking
- CI/CD and Infrastructure as Code experience (Terraform, containerisation with Docker, automated testing)
- Practical experience building MLOps from an early stage and selecting sensible tooling/patterns
- Ability to work across disciplines and strong communication skills for technical and non-technical stakeholders
- Ownership mentality including being on-call for production systems
- Experience with agentic and AI-accelerated coding tools (GitHub Copilot, Cursor, Claude Code)
- Familiarity with advertising technology, marketing analytics or media measurement use cases
- Knowledge of data governance, privacy and compliance requirements in data-driven products
- Experience in early-stage or scale-up environments building foundational capabilities
What We Do
The UK and Europe’s largest Radio & Outdoor company, Global is home to respected, national market-leading media brands broadcasting across the UK on DAB & FM and around the world on Global Player, including Heart, Capital, LBC, Capital XTRA, Capital Dance, Classic FM, Smooth, Radio X and Gold. Global Player allows listeners to enjoy all of Global’s radio brands, award-winning podcasts, and expertly curated playlists, in one place in app, on web and on smart speakers. Global is also one of the leading Outdoor companies in both the UK & Europe, with over 253,000 sites reaching 95% of the UK population. Global’s extensive and diverse outdoor portfolio encompasses Transport for London’s Underground network, almost all major UK airports including Gatwick, the UK’s largest portfolio of roadside posters and premium digital screens in prime locations, as well as the UK’s largest network of buses including all major cities. On-air, on Global Player and with our outdoor platforms combined, Global reaches 51 million individuals across the UK every week, including 26.3 million on the radio alone.
