Senior MLOps Engineer - Edge (AI)

Location: Barcelona or London offices; remote work available in Spain or the United Kingdom

Company

hirify.global builds sports technology products that help coaches and athletes capture video, analyze data, and share highlights.

What you will do

• Build and maintain scalable edge infrastructure for deploying machine learning models to fleets of smart cameras.
• Own model compilation pipelines that produce optimized, hardware-specific inference engines for devices such as Jetson Orin.
• Manage TensorRT compilation, FP16/INT8 precision trade-offs, calibration, and engine validation.
• Implement automated testing, telemetry, and monitoring for model drift, thermal impact, and inference latency.
• Design resilient update and recovery mechanisms for low-bandwidth networks, limited storage, and network failures.
• Collaborate with data scientists, embedded engineers, and product managers while mentoring teammates on Python tooling, Infrastructure-as-Code, and CI/CD.

Requirements

• Production experience building and operating MLOps pipelines for model deployment.
• Deep experience with CI/CD, Docker containerization, and Linux systems.
• Hands-on experience compiling and optimizing machine learning models for embedded hardware, including precision, quantization, and engine validation.
• Ability to design fault-tolerant architectures for large fleets of heterogeneous devices, including canary releases and safe rollbacks.
• Strong communication skills for collaborating with researchers and low-level embedded engineers.
• Initiative, ownership, and willingness to solve cross-functional technical problems.

Nice to have

• Experience with NVIDIA Jetson Orin, DeepStream SDK, or TensorRT.
• Familiarity with video pipelines, GStreamer, or ffmpeg.
• Experience with AWS IoT Greengrass, Balena, OTA updates, or fleet management solutions.
• Interest in sports technology, video analytics, or performance metrics.

Culture & Benefits

• Flexible vacation time, company-wide holidays, meeting-free days, and remote work options.
• Autonomous work environment with support for experimentation and ownership.
• Professional development resources and opportunities for career growth.
• Access to appropriate technology and hardware for office or remote work.
• Location-dependent medical and retirement benefits, plus an Employee Assistance Program and employee resource groups.