Senior MLOps Engineer - Edge AI & Hardware (Remote UK)

Tamarind Intelligence; Greater London; On-site; GBP 90,000 - 135,000; Full time

Benefits offered by this job

Flexible work options

Remote work options

Medical and retirement benefits

Job summary

Hudl is hiring a Senior MLOps Engineer for our Hardware Group to build and scale edge ML infrastructure powering Focus cameras. You will own edge deployment pipelines delivering models to thousands of devices globally and contribute to a platform that compiles models into hardware-specific inference engines.

You will work with Data Scientists, Embedded Engineers and Product Managers to enable robust, scalable deployments, automate testing on production devices, and monitor drift, latency and

Qualifications

• Production MLOps pipelines for deployment to production.
• Experience with CI/CD, containerization and Linux systems.
• Hands-on experience with edge inference and TensorRT.
• Ability to collaborate with researchers and embedded engineers.
• Experience deploying to thousands of devices with canary releases and rollbacks.

Responsibilities

• Build scalable edge infrastructure for model deployment.
• Own the model compilation platform for optimized engines.
• Collaborate with data scientists, embedded engineers, and product managers.
• Implement telemetry to monitor drift, latency and thermal impact.
• Design resilient update mechanisms for low-bandwidth environments.
• Mentor and lead by establishing best practices in Python tooling and CI/CD.

Skills

Production MLOps

Edge inference know-how

CI/CD & Docker

Linux systems

Cross-functional collaboration

Tools

TensorRT

Docker

GStreamer

FFmpeg

Jetson Orin

Job description

Hudl is hiring a Senior MLOps Engineer for our Hardware Group to build and scale edge ML infrastructure powering Focus cameras. You will own edge deployment pipelines delivering models to thousands of devices globally and contribute to a platform that compiles models into hardware-specific inference engines.

You will work with Data Scientists, Embedded Engineers and Product Managers to enable robust, scalable deployments, automate testing on production devices, and monitor drift, latency and