Aufgabe:
Main task of a Software Engineer with focus on MLOps is to create a robust, performant, and self-healing edge or cloud frameworks for the deployment of AI models. These include the connectivity to input sources and output sinks, with all steps monitored and containerized. As a Software Engineer with focus on MLOps, you will play a crucial role in building and scaling our ML systems, ensuring they are robust, scalable, and seamlessly integrated into our products and services. Design, build, and maintain the infrastructure and tools to deploy, monitor, and manage ML models in production. Work closely with data scientists and data engineers to create scalable, efficient, automated pipelines that ingest, process, and analyze large datasets. Responsibility for orchestrating and scheduling platforms (e.g., Dataiku) and solutions. Implement robust data validation and testing frameworks to ensure the accuracy and integrity of data and models. Collaborate with cross-functional teams to ensure that ML solutions meet business requirements and align with company goals. Responsible for interfacing to hardware such as cameras - GPIO ports or sensors.Qualifikation:
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 plusWeitere Angebote in den Bereichen: