MLOps Engineer

A strong passion for building robust and scalable ML platforms. A solid understanding of optimization techniques, multithreading, and distributed system concepts. A firm foundation in computer science principles, including data structures, algorithms, and algorithm complexity analysis. Familiarity with machine learning frameworks such as TensorFlow or PyTorch. Experience with experiment tracking and model management tools (e.g., MLflow, TensorBoard). Experience with containerization technologies (Docker, Kubernetes) and version control systems (e.g., GitHub). Excellent problem-solving, communication, and collaboration skills. Ability to work both independently and as part of a team.

Develop, and maintain the tools and infrastructure that support our ML model training, experimentation, and deployment workflows. Develop systems for efficient access to and management of large datasets. Create solutions for optimizing GPU utilization and resource allocation. Integrate and maintain experiment tracking and monitoring tools (e.g., MLflow, TensorBoard). Develop and implement the processes for deploying ML models to production environments. Collaborate closely with MLOps Engineers to understand their needs and provide effective solutions. Troubleshoot and resolve issues related to the ML platform. Stay current with the latest advancements in ML platform technologies and best practices.