Skills

Python Kubernetes Monitoring Research Training Machine Learning Programming

Job Specifications

My client is looking for an experienced ML Infrastructure Engineer to support the deployment, optimisation and scaling of advanced machine learning models in production environments. This role sits at the intersection of research and engineering, focused on ensuring models are reliably transitioned from experimentation through to large-scale deployment.

You will work closely with research and platform teams to build and maintain high-performance inference systems, improve deployment processes and help drive infrastructure improvements that enable faster model iteration and release cycles.

This is a strong opportunity to work on technically complex challenges within a fast-moving and highly collaborative environment.

The Role

Productionise machine learning models from research through validation, staging and live deployment
Build, maintain and optimise scalable inference infrastructure supporting high-throughput, low-latency workloads
Improve performance and reliability across GPU-based environments
Design and implement model serving and deployment workflows
Develop monitoring and observability tools to track system performance, errors and utilisation
Support data preparation and model integration as part of the wider development lifecycle
Collaborate with research, engineering and infrastructure teams to improve deployment efficiency and platform scalability
Evaluate and integrate third-party infrastructure and inference tooling where appropriate

Requirements

Proven experience deploying and maintaining ML inference systems in production environments
Strong programming experience in Python and familiarity with modern machine learning frameworks
Experience working with containerisation and orchestration technologies such as Kubernetes or similar
Exposure to distributed systems and cloud-based infrastructure
Experience supporting GPU workloads and performance optimisation
Strong troubleshooting skills across performance, scaling and system reliability
Comfortable working cross-functionally within research-led environments
Ability to operate in fast-paced teams with evolving technical priorities

Nice to Have

Experience building or improving model serving infrastructure
Understanding of distributed training or inference techniques
Experience debugging low-level performance or hardware-related issues
Exposure to real-time or latency-sensitive ML applications