Senior MLOps Engineer — Cloud & Edge AI (Security-Cleared)

Searchability NS&D; Brentford; On-site; GBP 57,000 - 95,000; Full time

Job summary

Searchability NS&D is seeking a Senior MLOps Engineer for a permanent role in Greater London. The role requires active clearance – SC or MoD DV – and sits four days on site in Brentford, with future travel to Cambridge.

You will design, build and maintain ML infrastructure, develop distributed training pipelines, and deploy models across cloud and edge environments, collaborating with AI researchers and software engineers.

Qualifications

• Strong Python/PyTorch and ML infrastructure experience.
• Experience with distributed ML training using Ray, DeepSpeed or PyTorch Lightning.
• Strong MLflow, model registry and ML lifecycle experience.
• Experience with TensorRT, ONNX, quantisation and model deployment.
• Strong Docker, CI/CD and GitHub Actions experience.
• Experience with Prometheus/Grafana monitoring and production observability.
• Cloud/edge AI deployment, with NVIDIA Jetson/DeepStream highly desirable.
• Knowledge of Kubernetes, Kubeflow, Metaflow and Kafka advantageous.
• Computer vision, aerial/satellite/ISR experience highly beneficial.

Responsibilities

• Design, build and maintain the infrastructure powering advanced AI systems from research to production.
• Develop distributed training pipelines and ML lifecycle infrastructure.
• Optimize models for deployment and support reliable AI systems across cloud and edge environments.
• Collaborate with AI researchers, software engineers and technical leadership.

Skills

MLOps

Python

PyTorch

Ray

DeepSpeed

PyTorch Lightning

MLflow

TensorRT

ONNX

Docker

Kubernetes

CI/CD

GitHub Actions

Prometheus

Grafana

Kafka

Metaflow

Edge AI

Computer Vision

Deep Learning

Tools

NVIDIA Jetson

DeepStream

Job description

Searchability NS&D is seeking a Senior MLOps Engineer for a permanent role in Greater London. The role requires active clearance – SC or MoD DV – and sits four days on site in Brentford, with future travel to Cambridge.

You will design, build and maintain ML infrastructure, develop distributed training pipelines, and deploy models across cloud and edge environments, collaborating with AI researchers and software engineers.