Define and evolve product strategy and roadmaps for Agentic AI and Data Intelligence products and platforms.
Research emerging technologies, industry standards, architectures, and best practices across AI agents, multi-agent systems, LLMs, reasoning models, tool use, workflow orchestration, enterprise
search, knowledge systems, and data intelligence.
Identify opportunities where AI agents can automate complex workflows, augment decision-making, and improve how users interact with enterprise data.
Translate emerging technology capabilities into practical enterprise use cases, product investments, and roadmap priorities.
Own technical product requirements, capabilities, APIs, integration patterns, success metrics, and non-functional requirements.
Partner closely with engineering, architecture, data science, data engineering, security, andbusiness teams to move capabilities from concept through production.
Define reusable platform capabilities for agent orchestration, tool integration, context management, memory, data access, reasoning, and human-in-the-loop workflows.
Drive build-vs-buy-vs-partner decisions for emerging AI and data technologies.
Define product capabilities spanning structured and unstructured data, semantic search, metadata, knowledge retrieval, RAG, vector search, and natural-language data interaction.
Support hands-on PoCs and prototypes involving single-agent and multi-agent workflows, tool calling, APIs, enterprise data sources, and automated business processes.
Define experiment hypotheses, evaluation datasets, success criteria, and production-readiness requirements.
Assess agent performance across dimensions such as task completion, accuracy, reasoning quality, latency, cost, reliability, security, and user experience.
Establish frameworks for evaluating and benchmarking Agentic AI systems beyond traditional model-level metrics.
Partner with risk, security, privacy, and governance teams to establish appropriate controls for autonomous and semi-autonomous AI systems.
Produce high-quality technical assessments, product requirements, architecture recommendations, benchmarking reports, decision documents, and technology landscape analyses.
Communicate product strategy, technical trade-offs, investment requirements, and measurable business outcomes to both technical and executive audiences.
Significant experience in AI product management, data platform product management, or related technical roles.
Strong understanding of Generative AI, LLMs, Agentic AI architectures, RAG, enterprise search, APIs, and modern data platforms.
Understanding of agent concepts including tool/function calling, planning, reasoning, context management, orchestration, memory, and human-in-the-loop patterns.
Experience working with structured and unstructured enterprise data, data APIs, metadata, search, retrieval, or analytics platforms.
Strong technical fluency with REST APIs, JSON, Python or similar scripting languages, cloud platforms, and AI/ML services.
Demonstrated ability to define technical products and collaborate deeply with engineering, architecture, data science, and data engineering teams.
Experience evaluating emerging technologies through PoCs, experimentation, benchmarking, and structured technical analysis.
Strong written and verbal communication skills with the ability to translate complex AI and data concepts into clear product and executive recommendations.
Experience designing or delivering enterprise AI agents, copilots, intelligent workflow automation, or AI-powered data products.
Experience with agent development and orchestration frameworks and emerging interoperability standards such as MCP.
Familiarity with vector databases, knowledge graphs, semantic layers, embedding models, enterprise search, data catalogs, and modern data platforms.
Experience developing evaluation frameworks for LLMs and agentic systems, including automated evaluation and human evaluation.
Understanding of enterprise AI governance, data governance, security, privacy, responsible AI, identity/access management, and observability.
Experience moving emerging AI capabilities through the lifecycle of research to experimentation to PoC to production to scaled enterprise adoption
