Senior AI Product Manager - Agentic AI & Data Intelligence

EXL•Phoenix, AZ

3h•$100,000 - $140,000•Onsite

About The Position

We are seeking a Senior AI Product Manager to lead strategy, evaluation, and development of next-generation Agentic AI and Data Intelligence products that combine autonomous AI agents, enterprise data, and intelligent workflows. The 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 AI capabilities. The ideal candidate brings technical depth and strategic product leadership to turn rapidly evolving AI technologies into secure, scalable, measurable enterprise products. The role is based onsite in Arizona, United States. The posted base salary range is $100,000–$140,000 per year, plus an annual bonus. Final compensation depends on factors such as skills, experience, internal pay equity, work location, market conditions, and role scope. The employer directs candidates to its benefits website for information about benefits but does not list specific benefits in the posting.

Requirements

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.
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 closely with engineering, architecture, data science, and data engineering teams.
Experience evaluating emerging technologies through proofs of concept, experimentation, benchmarking, and structured technical analysis.
Strong written and verbal communication skills, including the ability to translate complex AI and data concepts into clear product and executive recommendations.

Nice To Haves

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 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 research, experimentation, proof of concept, production, and scaled enterprise adoption.

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 for AI agents to automate complex workflows, augment decision-making, and improve user interactions with enterprise data.
Translate emerging technology capabilities into enterprise use cases, product investments, and roadmap priorities.
Own technical product requirements, capabilities, APIs, integration patterns, success metrics, and non-functional requirements.
Partner with engineering, architecture, data science, data engineering, security, and business teams to advance 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 capabilities spanning structured and unstructured data, semantic search, metadata, knowledge retrieval, RAG, vector search, and natural-language data interaction.
Support hands-on proofs of concept 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 in 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 controls for autonomous and semi-autonomous AI systems.
Produce 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 technical and executive audiences.