Machine Learning Engineer

SR2; City Of London; On-site; GBP 92,000 - 115,000; Full time

Job summary

SR2 is seeking a Senior MLOps Engineer to build the ML platform that takes research into production. You will work with AI developers and software engineers to deploy reliable ML products in cloud and on edge hardware for a long-term defence/security programme.

This hands-on role offers influence over ML platform architecture and requires active SC clearance. Expect design of pipelines, model registries, and CI/CD for scalableML workloads.

Qualifications

• We require hands-on MLOps experience taking models from R&D to production.
• Experience with distributed training frameworks and experiment tracking systems.
• Proficiency in containerised deployment and CI/CD for ML workloads.

Responsibilities

• Design and run ML training pipelines at scale across cloud and edge.
• Build a registry and data/versioning for models and experiments.
• Implement monitoring, observability and CI/CD for ML systems.
• Containerise workloads and enable multi-architecture builds.
• Lead orchestration of ML workflows and event-driven pipelines.

Skills

MLOps

Distributed training

Experiment tracking

Model registry

Docker

CI/CD

Monitoring

Edge deployment

Kubernetes

Cloud & edge

Education

MSc/PhD in Computer Science or ML

Tools

MLflow

Ray

PyTorch Lightning

ONNX

TensorRT

GStreamer

Kubernetes

Kubeflow

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 (e.g. Ray, DeepSpeed, 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. GitHub Actions, GitLab CI or similar)
• Monitoring and observability (e.g. Prometheus, Grafana)
• Active SC clearance

Nice to have

• Edge AI deployment, ideally NVIDIA Jetson and DeepStream
• 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, 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