Staff Platform Engineer - AI/ML

Develops and implements innovative machine learning algorithms and models, optimizing their performance to address complex business scenarios and improve operational efficiency.,Collaborates with cross-functional teams to identify opportunities for leveraging machine learning technologies, translating business challenges into technical solutions, and presenting findings to diverse audiences.,Conducts and oversees state of the art research in machine learning and artificial intelligence, authoring scientific papers, presenting at industry conferences, and contributing to the organization's reputation as a leader in the field.,Executes on machine learning development tasks or projects, requiring advanced problem solving, decision making and strategic thinking with some ambiguity.,Drives decisions on complex issues to develop clear, actionable recommendations for management, ensuring alignment on processes, tools and services with impact across other areas.,Leverages advanced and creative skills to resolve complex machine learning development related problems, fostering cross functional collaboration to identify and implement innovative solutions.,Leads and facilitates cross functional collaboration efforts, fostering strong internal relationships across the organization and external partnerships to achieve impactful business outcomes.

Strong and relevant experience designing and implementing distributed systems and Machine Learning systems.,Working with building and maintaining DevOps pipeline such as Jenkins, GitHub actions.,Previous experience with MLOps orchestration tools such as AirFlow, KubeFlow, Dagster, Flyte, or 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.,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, such as AWS and Azure.,Familiarity with machine learning frameworks such as PyTorch, TensorFlow and/or similar.