Equivalent job experience or a Master's in Computer Science, Engineering, Physics, or a related field At least 2 years of experience in a role focusing on ML model deployment, management, and scalability Solid knowledge of Linux and networking protocols (udp, grpc, mqtt), including kernel configurations and yocto builds Expert knowledge in microservice architecture: docker, Kubernetes, streaming (kafka), logging and database (redis, sql) architectures General knowledge about MLOps concepts (e.g., reproducibility, testing, monitoring), with a focus on connecting different platforms (e.g., labeling platform, artifact store, training infrastructure) to drive automation Experienced with model deployment frameworks such as NVIDIA Triton inference server, and with programming streaming solutions with deepstream for Nvidia edge hardware: Jetson Xavier / Orin Expert knowledge in modern Python development, including knowledge about best practices package management, linting / formatting, data validation, general code design (design patterns), parallelization, and CI / CD with unit and integration tests Profound experience with cloud services (e.g., AWS, Azure, Google) Advanced knowledge in C, C++. Familiar with.NET is a plus