Senior MLOps Engineer - Edge

Hudl · Londonsenior

En corto

▸Ingeniero MLOps Senior enfocado en despliegue de modelos de IA en dispositivos Edge como cámaras inteligentes.
▸Diseña y mantiene pipelines de compilación y despliegue de modelos optimizados (TensorRT, FP16/INT8) para hardware como Jetson Orin.
▸Destaca por liderar infraestructura escalable con automación, telemetría y resiliencia en entornos de bajo ancho de banda y almacenamiento limitado.

Fluency in English is required.

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¿Qué piden?

✓Experiencia probada en MLOps en producción con CI/CD, Docker y Linux.
✓Conocimiento práctico de compilación y optimización de modelos para hardware embebido, especialmente con TensorRT.
✓Habilidades en diseño de pipelines de despliegue a flotas de dispositivos Edge.
✓Capacidad para trabajar con equipos multidisciplinarios (Data Scientists, Ingenieros embebidos, Product Managers).
✓Experiencia en automatización de pruebas, telemetría y monitoreo de modelos en entornos reales.
✓Capacidad de mentoría y liderazgo técnico en buenas prácticas de desarrollo y DevOps.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonDockerLinuxTensorRTJetson OrinCI/CDInfrastructure-as-CodeModel compilationInference optimizationTelemetry

¿A quién escribirle en Hudl?

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At Hudl, we build great teams. We hire the best of the best to ensure you’re working with people you can constantly learn from. You’re trusted to get your work done your way while testing the limits of what’s possible and what’s next. We work hard to provide a culture where everyone feels supported, and our employees feel it—their votes helped us become one of Newsweek's Top 100 Global Most Loved Workplaces . We think of ourselves as the team behind the team, supporting the lifelong impact sports can have: the lessons in teamwork and dedication; the influence of inspiring coaches; and the opportunities to reach new heights. That’s why we help teams from all over the world see their game differently. Our products make it easier for coaches and athletes at any level to capture video, analyze data, share highlights and more. Ready to join us? Your Role We're hiring a Senior MLOps Engineer for our Hardware Group to build and scale the machine learning infrastructure that powers Focus, our line of smart cameras. You'll own the edge deployment pipelines that transport neural networks from training clusters to tens of thousands of devices globally, and contribute to the platform that compiles trained models into optimised inference engines for devices like the Jetson Orin, building the "nervous system" for the next generation of automated sports capture. As a Senior MLOps Engineer, you'll: • Build scalable Edge infrastructure. You'll design, develop, and maintain the delivery systems that enable us to deploy models to fleets of devices. • Own the model compilation platform. You'll build and maintain the pipeline that takes trained models and produces optimised, hardware-specific inference engines — managing TensorRT compilation, precision trade-offs (FP16/INT8), calibration, and engine validation to ensure models run reliably and efficiently on target devices. • Work with cross-functional teams. You'll collaborate with Data Scientists, Embedded Engineers, and Product Managers to ensure smooth integration of complex features and capabilities • Drive automation and reliability. You'll implement infrastructure to silently test candidate models on production devices and build telemetry pipelines to monitor drift, thermal impact, and inference latency in the wild. • Solve complex physical challenges. You'll tackle the unique constraints of the edge - building resilient update mechanisms for low-bandwidth environments, optimising for limited storage, and ensuring devices recover gracefully from network failures. • Mentor and lead. You'll share your expertise to establish best practices in Python tooling, Infrastructure-as-Code, and CI/CD, guiding the team toward a more robust, automated future. We'd like to hire someone for this role who lives near our offices in London or Barcelona, but we're also open to remote candidates in the UK and Spain. Must-Haves • Production MLOps expertise. You've played a key role in building and operating pipelines that deploy models to production, with deep experience in CI/CD, containerization (Docker), and Linux systems. • Edge inference & compilation know-how. You have hands-on experience compiling and optimising models for embedded hardware - ideally with TensorRT - and understand the practical impli

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