AI Product Manager / Strategist - Agentic AI & Data Intelligence

Job Title: AI Product Manager / Strategist - Agentic AI & Data Intelligence

• 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