Machine Learning Engineer

SR2 - Socially Responsible Recruitment; Greater London; On-site; GBP 92,000 - 115,000; Full time

Job summary

SR2 - Socially Responsible Recruitment is seeking a Senior MLOps Engineer to design and run infrastructure that moves ML from research to production. You’ll work with AI developers, software engineers and leadership to deploy reliable cloud and edge ML solutions for a long-term defence program in East London.

This hands-on role influences ML platform architecture and requires active SC clearance. The position is a 6‑month initial contract, four days per week on-site in East London, with

Qualifications

• Senior MLOps/ML engineer with production experience.
• Experience with distributed training (Ray/DeepSpeed/PyTorch Lightning).
• Experience with experiment tracking and model registries (MLflow).
• Experience with CI/CD and containerization (Docker).
• Active SC clearance.

Responsibilities

• Design and maintain ML training pipelines for production.
• Build experiment tracking, model registry and data versioning.
• Optimize models for production and edge deployment.
• Containerise ML workloads 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.

Skills

MLOps

ML engineering

Distributed training

Experiment tracking

Model optimisation

Docker

CI/CD tooling

Monitoring observability

Edge deployment

Kubernetes

Tools

Ray

DeepSpeed

PyTorch Lightning

MLflow

Job description

Machine Learning/ML Ops Engineer
Outside IR35: £500 - 625 p/d
Active SC Clearance Required
4 days a week into East London
6 months initial contract, long term programme

The Role

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.

What you'll do

• 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

What you'll bring

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

Nice to have

• Edge AI deployment, ideally NVIDIA Jetson and DeepStream
• Computer vision or video pipelines (eg GStreamer)
• Kubernetes, Kubeflow, Metaflow or similar orchestration tools
• Kafka or other event‑driven architectures
• Data versioning tools (eg DVC, DeepLake)
• 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