AI Product Manager

Job Overview

Pay Range: $55/hr - $60/hr

Requirement/Must Have

• A bachelor's degree in Engineering, Computer Science, or an equivalent qualification.
• Overall experience of 10+ years with a significant portion in a product management capacity.
• Strong practical experience in leading large multi-team product initiatives, preferably for AI/ML or technical cloud-based products (e.g., AWS, Google Cloud).
• A deep understanding of the AI/ML development lifecycle, including LLMs, agentic architectures, RAG pipelines, and MLOps.
• Demonstrated ability to engage with engineering teams, fellow product managers, and end-users to understand complex workflows, identify opportunities, and create clear prioritized product roadmaps.
• Excellent oral and written communication skills with the ability to articulate a complex technical vision to both technical and non-technical senior leadership.
• Proven ability to work strategically and collaboratively across teams in fast-paced and constantly changing environments.
• Strong analytical skills with experience defining and tracking product metrics to make data-informed decisions.
• Proven ability to influence cross-functional teams and demonstrate product leadership without direct authority.

Responsibilities

• Define Product Vision and Strategy: Develop and champion the product vision, strategy, and roadmap for agentic AI systems.
• Lead the Product Lifecycle for LLMs: Guide the end-to-end product lifecycle from Client to launch and iteration.
• Drive Integration and Ecosystem Strategy: Define and prioritize integrations with key runtime ecosystems including observability, incident management, and deployment systems.
• Champion the Voice of the Customer: Collaborate directly with production engineers and application teams through deep user research.
• Own AI Safety, Reliability, and Governance: Establish and own the product framework for AI safety, reliability, and governance.
• Manage Product Performance and Scale: Define and monitor product SLOs and key performance indicators (KPIs) for cost, latency, and user satisfaction.
• Oversee Data and Knowledge Strategy: Own the product strategy for the RAG pipeline.
• Drive Product Excellence and Raise the Bar: Lead product design reviews, champion data-driven experimentation, and instill high-quality product management practices.