Senior MLOps Engineer

Senior MLOps Engineer

Salary: £70,000 to £100,000, depending on experience
Location: West London
Working arrangement: Predominantly office and customer-site based, with some remote working available
Employment: Permanent, full-time

MFK Recruitment is recruiting a Senior MLOps Engineer for an innovative UK technology company developing advanced Artificial Intelligence and Machine Learning solutions for defence, security and other demanding real-world environments.

The company specialises in computer vision, perception and autonomy, combining modern deep learning with neuroscience-inspired technology to create AI systems that are accurate, robust and reliable.

MFK Recruitment has successfully recruited four Engineers to this company over the past five years, and all four are still with the business. This speaks volumes about the company’s culture, technical challenges and long-term opportunities.

Senior MLOps Engineer role

The Senior MLOps Engineer will develop and improve the infrastructure that takes advanced AI and computer vision models from research through to reliable production deployment.

You will bridge the gap between Machine Learning research and operational delivery, ensuring models can be trained efficiently, deployed reliably and monitored effectively.

This is a hands-on engineering role covering distributed training, experiment tracking, model registries, containerised deployments, inference optimisation and production monitoring.

You will work with cloud platforms and edge devices, including GPU-accelerated NVIDIA Jetson systems.

The position is predominantly office and customer-site based in West London, although some remote working will be available.

The company is happy to consider candidates with varying levels of experience, with the salary offered reflecting the successful candidate’s technical background and level of seniority.

Senior MLOps Engineer responsibilities

• Develop distributed training pipelines for large-scale model development.
• Implement and manage Machine Learning experiment-tracking and model-registry infrastructure.
• Optimise models for production using inference acceleration, quantisation and pruning.
• Build containerised deployment pipelines for cloud, x86 and edge-device architectures.
• Develop and maintain CI/CD pipelines for automated testing, building and deployment.
• Manage GPU-accelerated edge deployment and video-analytics infrastructure.
• Develop event-driven processing systems.
• Work closely with AI Engineers, Software Engineers and customers to productionise models.
• Contribute to technical planning, architecture decisions and engineering best practices.

Essential experience

• Strong Python development skills.
• Experience with Machine Learning experiment tracking and model registries using MLflow, Weights & Biases, Neptune, ClearML or a similar platform.
• Experience with distributed training frameworks such as Ray, DeepSpeed, Horovod or PyTorch FSDP.
• Hands-on experience optimising models for inference using TensorRT, ONNX Runtime, OpenVINO, quantisation or pruning.
• Strong Docker and containerisation skills, including multi-stage or multi-architecture builds.
• Experience building CI/CD pipelines for Machine Learning systems.
• Experience with monitoring and observability tools such as Prometheus, Grafana, Datadog or similar.
• Linux systems administration and shell-scripting experience.
• Strong software engineering practices, including Git, testing and code reviews.
• Experience delivering AI or Machine Learning systems into production.

Desirable experience

• NVIDIA Jetson or other edge AI platforms.
• NVIDIA DeepStream, GStreamer or FFmpeg.
• Kubernetes, Kubeflow, ECS, Nomad or other container-orchestration platforms.
• Airflow, Prefect, Dagster, Metaflow or similar workflow tools.
• Kafka, Pulsar, Redis Streams, RabbitMQ or other event-driven technologies.
• Triton Inference Server, Ray Serve, TorchServe, BentoML or Seldon Core.
• DVC, LakeFS, Delta Lake, Feast or other data-versioning and feature-store technologies.
• Terraform, Pulumi, CloudFormation or Ansible.
• GPU cluster management or high-performance computing.
• Aerial, satellite or ISR imagery.

Security clearance

Candidates must be eligible to obtain UK BPSS and Security Check clearance. Existing clearance would be advantageous but is not essential for every appointment.

Applicants for SC clearance are normally expected to have lived in the UK for the previous five years. A shorter period of UK residency or time spent overseas will not automatically prevent clearance, but eligibility will be assessed individually by the sponsoring authority.