Director ML Platform Engineering

Design, build, and optimize machine learning deployment tools and automation systems for data and ML applications.,Design and implement best practices and standards for data and machine learning pipelines across the organization.,Collaborate with engineers and machine learning researchers to automate code analysis, build, integration, and deployment of ML applications.,Support applications and projects with infrastructure design decisions and monitoring solutions.,Build highly scalable, resilient cloud and on-premise systems for hosting machine learning systems using state-of-the-art technologies.

Strong and relevant experience designing and implementing distributed systems and Machine Learning systems.,Experience with building and maintaining DevOps pipelines (e.g., Jenkins, GitHub actions).,Previous experience with MLOps orchestration tools (e.g., AirFlow, KubeFlow, Dagster, Flyte, MetaFlow).,In-depth knowledge of various stages of the machine learning application deployment process.,Experience with building tools and applications to automate various infrastructure and DevOps tasks.,Proficiency with programming languages such as Python, Bash, or JavaScript.,Solid understanding of the UNIX operating system.,Experience implementing monitoring solutions to identify system bottlenecks and production issues.,Knowledge of professional software engineering best practices for the full software development life cycle, including testing methods, coding standards, code reviews, and source control management.,Hands-on experience building and deploying hybrid environments on-prem and major cloud environments (e.g., AWS and Azure).,Familiarity with machine learning frameworks such as PyTorch, TensorFlow and/or similar.