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Responsibilities
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.
Eligibility Requirements
A bachelor’s degree in Engineering, Computer Science, or an equivalent qualification; an overall experience of ~10-15 years with a significant portion in a product management capacity.
Must have 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. While hands-on coding is not required, the ability to engage in deep technical discussions is essential.
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. 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.
Eligibility Requirements
A bachelor’s degree in Engineering, Computer Science, or an equivalent qualification; an overall experience of ~10-15 years with a significant portion in a product management capacity.
Must have 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. While hands-on coding is not required, the ability to engage in deep technical discussions is essential.
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.