| Job Description: |
We are seeking an AI Product Manager – Agentic AI & Data Intelligence to lead the strategy,
evaluation, and development of next-generation AI products that combine autonomous AI agents,
enterprise data, and intelligent workflows.
This role sits at the intersection of Agentic AI, Generative AI, data platforms, product strategy, and
engineering. The Product Manager will identify emerging technologies, define product capabilities and
roadmaps, evaluate third-party and open-source solutions, and work hands-on with engineering and data
teams to prototype and validate new AI capabilities.
The ideal candidate combines strong technical depth with strategic product leadership and can translate
rapidly evolving AI technologies into secure, scalable, measurable enterprise products.
Responsibilities
• 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 decisionmaking, 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, and
business 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.
Qualifications
• 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 |
