ML Ops Engineer

Salary: £62,000 - 102,000 per year

Requirements:

• 3–7 years of professional experience in ML Ops, Data Engineering, or adjacent backend roles
• Strong production Python skills, including clean APIs, testing, and performance awareness
• Experience deploying and operating ML models in production environments
• Solid understanding of model training vs. inference requirements
• Solid understanding of batch vs. streaming data pipelines
• Solid understanding of failure modes in data-driven systems
• Hands-on experience with at least one modern orchestration or workflow system
• Comfort working with cloud infrastructure and containerized workloads
• Ability to reason about system design, not just tool usage
• Experience with PyTorch or similar frameworks
• Experience with experiment tracking and structured datasets

Responsibilities:

• Own the reliability, scalability, and operational integrity of our machine-learning systems in research and production
• Design and operate data pipelines that feed models
• Build tooling that trains, deploys, monitors, and retrains models
• Take models from experimentation to production-grade systems with clear SLAs, reproducibility guarantees, and observable behaviour
• Productionize models through packaging, deployment, versioning, and rollback
• Design CI/CD pipelines for ML, including training validation and deployment
• Implement model monitoring for data drift, prediction drift, and performance decay
• Manage experiment tracking and reproducibility
• Build and maintain batch and near-real-time data pipelines
• Ensure data quality, schema evolution, and lineage across systems
• Design datasets and feature pipelines that support both training and inference
• Operate pipelines with clear reliability and latency expectations
• Define and meet availability, latency, and freshness targets for ML services
• Debug production issues across data, infrastructure, and model layers
• Improve system robustness through automation and observability
• Collaborate with platform and security teams on access, secrets, and compliance
• Write production-grade Python used in long-running services and pipelines
• Establish testing, validation, and release practices for ML systems
• Make trade-offs explicit between research flexibility and production stability

Technologies:

• Backend
• CI/CD
• Cloud
• Support
• Model Training
• PyTorch
• Python
• Security
• Model Serving

More:

We are hiring an ML Ops Engineer to work at the intersection of data engineering and ML infrastructure, helping us make our machine-learning systems reliable, scalable, and production-ready. You will work closely with research engineers and product teams in our London and Warsaw locations, with a full-time role focused on operational excellence rather than research. We value engineers who care about data correctness, observability, and building ML systems that do not silently fail. We are an equal opportunities employer and positively encourage applications from suitably qualified and eligible candidates regardless of gender, sexual orientation, marital or civil partner status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability or age.

last updated 34 week of 2026