Senior AI Product Manager - Agentic AI & Data Intelligence

Work Location: Arizona, United States Work Mode      : onsite Pay Range       :$100K-$140K /Yr Base + Annual Bonus  The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits [https://www.exlservice.com/us-careers-and-benefits]     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 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, 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.