Leadership and Team Management: Direct and manage a team of cloud architects and engineers in designing and building our ML Ops platform. Foster a collaborative and innovative environment and ensure alignment with project goals.
Responsible AI Solutions: Develop solutions focused on Responsible AI, including but not limited to explainability, data drift detection, and fairness metrics. Ensure adherence to ethical AI practices.
Platform Development: Enhance the MLOps platform to improve developer experiences for data and ML engineers. This includes streamlining workflows and integrating advanced tools and technologies.
Collaboration with AI/ML Experts: Liaise with specialists in Computer Vision, NLP, Forecasting, Large Language Models (LLM), predictions, synthetic data, and other AI/ML domains. Incorporate the latest advancements from these fields into the MLOps platform.
AI/ML Reliability and Observability: Work closely with the AI/ML Reliability engineering team to develop components that ensure the reliability and observability of our platform.
Cross-Disciplinary Knowledge: While primarily focused on AI/ML, this role also requires knowledge in related disciplines such as data sciences and health/biology sciences.
Experience in Platform Development: Candidates should have prior experience working with platform teams, especially in the data and AI/ML space.
Technical Skills and Experience:
Cloud Platform Expertise: Proficiency in cloud platforms is essential. Experience in expanding platforms to on-premises and edge deployments is a plus.
Cross-functional Collaboration: Ability to work closely with peer cloud teams, data scientists, and product managers to align platform development with organizational objectives.
Requirements & Skills:
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field is preferred, but not mandatory for candidates demonstrating significant experience and a proven track record in the field of AI/ML and platform development.; Master’s or higher degree is a plus.
Proven experience in managing and leading cloud architecture and engineering teams.
Strong background in AI/ML or Data Sciences technologies and platform development.
Demonstrated capability in leading Responsible AI initiatives.
Excellent communication, leadership, and project management skills.