Machine Learning Engineer (London)

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Base pay range

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Were hiring an MLOps Engineer to create and maintain the systems that keep advanced AI models running smoothly at scale. In this role, youll work alongside data science and engineering teams to design production-ready pipelines, automate deployments, and ensure models remain performant and reliable in real-world conditions.

Youll play a hands-on role in shaping the operational backbone of large language model (LLM)powered features, building the frameworks and tooling needed to deliver them to users with confidence.

Key Impact Areas

Build automated workflows for model training, evaluation, deployment, and monitoring.
Establish best practices for CI/CD in machine learning, including model testing and version control.
Monitor and track model behaviour in production, detecting drift and ensuring data quality.
Partner with Data Scientists to containerise and operationalise models, including those with advanced retrieval or graph-based components.
Configure and optimise cloud-based ML infrastructure (AWS, GCP, or Azure) for performance and scalability.
Apply Infrastructure as Code (Terraform, Pulumi, etc.) to provision and manage environments.
Integrate AI services into broader application architectures, working closely with product and engineering.
Drive continuous improvement across the ML lifecycle, from A/B testing to incident response.

What Youll Bring

3+ years in MLOps, DevOps, or software engineering focused on ML systems.
Familiarity with LLM deployment and operational support.
Experience with Docker, Kubernetes, and cloud ML services.
Understanding of vector databases and search optimisation strategies.
Hands-on knowledge of monitoring and observability tools (e.g., Prometheus, Grafana).

Seniority level

Seniority level
Mid-Senior level

Employment type

Employment type
Full-time

Job function

Job function
Information Technology
Industries
IT Services and IT Consulting

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