Machine Learning Engineer

We're looking for a Senior MLOps Engineer to build and run the infrastructure that takes machine learning models from research into production. You'll work alongside AI developers, software engineers and technical leadership to turn R&D into reliable, deployable products in the cloud and on edge hardware. The work is for a long-term programme in the defence and security sector.

This is a hands‑on role, and you'll have real influence over the architecture of the ML platform.

• Design and maintain ML training pipelines, including distributed training at scale
• Build experiment tracking, model registry and data versioning capability
• Optimise models for production and edge deployment
• Containerise ML workloads, including multi‑architecture builds for cloud and edge targets
• Implement CI/CD for ML systems
• Build monitoring and observability for models in production
• Orchestrate ML workflows and event‑driven pipelines

• Strong commercial MLOps or ML engineering experience, taking models from R&D into production
• Distributed training frameworks (e.g. Ray, Deep Speed, PyTorch Lightning)
• Experiment tracking and model registries (e.g. MLflow or similar)
• Model optimisation (e.g. ONNX, TensorRT, quantisation)
• Docker and containerised deployment
• CI/CD tooling (e.g. Git Hub Actions, Git Lab CI or similar)
• Monitoring and observability (e.g. Prometheus, Grafana)
• Active SC clearance

• Edge AI deployment, ideally NVIDIA Jetson and Deep Stream
• Computer vision or video pipelines (e.g. GStreamer)
• Kubernetes, Kubeflow, Metaflow or similar orchestration tools
• Kafka or other event‑driven architectures
• Data versioning tools (e.g. DVC, Deep Lake)
• Experience with imagery, geospatial or sensor data
• Previous defence, government or security‑sector programmes
• MSc or PhD in Computer Science, ML or a related field, or equivalent commercial experience