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New York, NY, USA
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Job Description:
What you’ll do:
Define Product Vision and Strategy: Develop and champion the product vision, strategy, and roadmap for agentic AI systems. Define what success looks like by translating user pain points into clear product requirements, user stories, and objective functions linked to reliability, risk reduction, and cost savings.
Lead the Product Lifecycle for LLMs: Guide the end-to-end product lifecycle, from discovery to launch and iteration. Establish the business-facing evaluation framework for foundational and open-source LLMs, and prioritize the development of retrieval pipelines, prompt synthesis, and validation loops to meet user needs in production operations.
Drive Integration and Ecosystem Strategy: Define and prioritize integrations with key runtime ecosystems, including observability, incident management, and deployment systems. Articulate the value proposition for each integration to enable automated diagnostics, runbook execution, and intelligent post-incident analysis.
Champion the Voice of the Customer: Collaborate directly with production engineers and application teams through deep user research. Translate their production challenges into a prioritized AI product roadmap and ensure the solutions delivered are auditable, effective, and solve real-world problems.
Own AI Safety, Reliability, and Governance: Establish and own the product framework for AI safety, reliability, and governance. Work with engineering, legal, and compliance teams to define product policies, deterministic fallbacks, and rollback strategies, ensuring all solutions adhere to the highest standards of safety and least-privilege access.
Manage Product Performance and Scale: Define and monitor product SLOs and key performance indicators (KPIs) for cost, latency, and user satisfaction. Prioritize engineering efforts to optimize performance through techniques like prompt engineering, caching, and model routing to meet stringent business requirements.
Oversee Data and Knowledge Strategy: Own the product strategy for the RAG pipeline. Define the scope of required domain knowledge, set product requirements for data quality and validation, and establish feedback loops to maintain knowledge freshness and relevance.
Drive Product Excellence and Raise the Bar: Lead product design reviews, champion data-driven experimentation, and instill high-quality product management practices. Mentor peers and stakeholders on AI product management, evaluation methodologies, and safe deployment patterns to foster a culture of innovation and excellence
Any Graduate
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