Senior MLOps Engineer – Computer Vision & AI Machine Learning Infrastructure | Model Deployment | Edge AI
Employment: Permanent, full-time

About the Company An innovative AI and Machine Learning company developing advanced technologies for defence, security and other high-performance applications. The team specialises in computer vision, deep learning and autonomous systems, developing AI solutions for deployment across cloud infrastructure and edge devices.
The Role We're looking for an experienced Senior MLOps Engineer to build and improve the infrastructure that takes machine learning models from research and development into production.
You'll help ensure computer vision models can be trained at scale, optimised for inference and deployed reliably across cloud and edge environments, including GPU-powered devices.
This is a hands-on engineering role focused on distributed training, model optimisation, containerisation, CI/CD and production monitoring. You'll work closely with AI developers and software engineers to build scalable, reliable ML infrastructure.
Key Responsibilities

• Develop and maintain distributed model training pipelines.
• Build experiment tracking and model registry infrastructure.
• Optimise models for inference using TensorRT, ONNX and quantisation.
• Develop containerised deployment pipelines for multiple architectures, including edge devices.
• Build and maintain CI/CD pipelines for automated testing, building and deployment.
• Implement production monitoring, observability, metrics and alerting.
• Manage edge deployment infrastructure for GPU-accelerated video analytics.
• Develop ML workflow orchestration and event-driven processing systems.
• Implement data versioning and reproducible training pipelines.
• Collaborate with AI engineers to productionise computer vision models.
• Contribute to infrastructure architecture, engineering best practices and technical planning.

Essential Skills & Experience MLOps & Infrastructure

• Experience with ML experiment tracking and model registries, such as MLflow, Weights & Biases or ClearML.
• Distributed training frameworks, such as Ray, DeepSpeed, Horovod or PyTorch FSDP.
• Model inference optimisation using TensorRT, ONNX Runtime or quantisation techniques.
• Strong Docker and containerisation experience, including multi-architecture builds.
• CI/CD pipelines for ML systems, using tools such as GitHub Actions, GitLab CI or Jenkins.
• Monitoring and observability tools, such as Prometheus and Grafana.

Software Engineering

• Expert Python development skills.
• Experience developing REST APIs using FastAPI, Flask or Django.
• Strong software engineering practices, including Git, testing and code reviews.
• Linux systems administration and shell scripting.
• Familiarity with PostgreSQL or other relational databases.

Machine Learning

• Understanding of deep learning training workflows, ideally with PyTorch.
• Experience with computer vision models and inference pipelines.
• Familiarity with GPU computing and CUDA environments.
• Experience delivering production AI systems.

Desirable Experience

• Edge AI: NVIDIA Jetson, Raspberry Pi, Intel NCS or Coral TPU.
• Video analytics: NVIDIA DeepStream, GStreamer or FFmpeg.
• Workflow orchestration: Metaflow, Kubeflow Pipelines, Airflow or Prefect.
• Event-driven systems: Apache Kafka, RabbitMQ or Redis Streams.
• Model serving: Triton Inference Server, Ray Serve, TorchServe or BentoML.
• Container orchestration: Kubernetes, Docker Swarm or Amazon ECS.
• Data versioning: DVC, LakeFS or Delta Lake.
• Infrastructure as Code: Terraform, Pulumi or Ansible.
• GPU clusters, high-performance computing and distributed training.
• Real-time video analytics and aerial, satellite or ISR imagery.
• Experience working within defence, government or national security environments.

Education An MSc or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision or a related discipline is desirable. Equivalent commercial experience will also be considered.
Security Requirements Candidates must be eligible to obtain UK security clearance. BPSS clearance is required, with SC clearance or higher desirable.
Why Join?

• Build and scale ML infrastructure supporting advanced AI technology.
• Work across the full machine learning lifecycle, from distributed training to edge deployment.
• Influence infrastructure architecture and engineering decisions.
• Collaborate closely with AI developers and software engineers.
• Contribute to advanced computer vision and deep learning applications within demanding defence and security environments.

We are recruiting now and operate a streamlined interview process.