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High applicant density due to metro location, popular senior ML role title, and broad skillset requirements.
High because the role requires specialized LLM, RAG, vector DB, MLOps, and enterprise AI governance expertise.
High because the JD mandates 8+ years, specific GenAI/LLM/MLOps leadership, and cloud/platform expertise.
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Lead the design and delivery of enterprise-scale AI, machine learning, and Generative AI solutions, including architecture, standards, and scalable product development.
Drive the AI roadmap aligned to business objectives, including advanced GenAI applications using RAG, AI agents, and vector databases.
Provide technical leadership and mentorship across cross-functional teams, lead architecture reviews, and implement MLOps/LLMOps frameworks ensuring governance and compliance.
8+ years in designing and delivering enterprise Data Science, Machine Learning, and Generative AI solutions.
Experience with LLMs, Retrieval-Augmented Generation architectures, AI agents, and cloud AI platforms such as Snowflake and AWS.
Proficient in Python and SQL; experienced in MLOps/LLMOps practices including CI/CD, monitoring, and model lifecycle management.
Work Experience Required: Minimum 8 years in relevant AI and Data Science leadership roles.
Proven ability to translate complex business challenges into AI-powered solutions while collaborating with stakeholders across disciplines.
Experience leading technical teams and influencing AI strategy at enterprise scale, familiar with AI governance, privacy, and compliance frameworks.
Strong engineering leadership with hands-on experience in cloud-native AI architectures, advanced GenAI technologies, and enterprise AI platform adoption.