Sr. Product Manager

NBCUniversal•Universal City, CA

2d•$130,000 - $170,000

About The Position

We're looking for a product manager who embeds directly with engineering, product, and business teams to build and ship AI products that directly serve our NBCUniversal businesses. You'll work side-by-side with Enterprise Business Solutions Product and Program teams to identify high-value problems, rapidly prototype and deploy production agentic solutions to drive business impact. As a Senior Product Manager in the Enterprise Business Solutions AI Product team, you will own the full lifecycle of agent products — from discovery through deployment and ongoing iteration. You'll build intelligent workflows that automate program operations, surface insights from complex data, and enable teams across NBCUniversal to work with agentic solutions. Your work will have a direct, measurable business impact on our employees’ experience and effectiveness in their work outputs. This role is a blend of technical, design, education, and product thinking. You will understand business needs deeply, identify where AI creates leverage, write code, and ship solutions that will drive real business impact.

Requirements

Bachelor’s degree in data analytics, computer science, or related field.
8+ years of experience in digital product development or a closely related field
Exceptional communication and collaboration skills, with the ability to influence stakeholders at all levels.
Proven expertise in developing and deploying Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architecture (prompt engineering, tool use, agent orchestration), NLP, machine learning algorithms, and big data analytics.
Analytical skills for identifying user patterns, insights, and performance metrics.
Ability to gather and comprehend complex requirements and challenges across all business levels.
Ability to convey complex technical issues to both technical and non-technical audiences, with ability to define requirements and iterate on solutions
Hands-on experience with understanding and building products on large data sets and enterprise data architectures requiring systems integration, data governance, and security compliance.
Hands-on experience with AI platforms, cloud services, and analytics tools (e.g., Python, Azure, AWS, Google Cloud)
Expertise in SDLC process, scoping, iteration planning, user story writing, rollout management.

Nice To Haves

Experience building multi-agent systems or complex LLM orchestration pipelines
Familiarity with evaluation frameworks for LLM outputs (accuracy, hallucination detection, quality scoring)
Technical skills — ability to build end-to-end prototypes from data pipelines through user-facing interfaces
Background in ERP systems, media, commerce, enablement, or enterprise operations domains
Experience with MCP (Model Context Protocol) or similar tool-use frameworks
Experience embedded with business teams to build solutions (solutions engineering, field engineering, or similar)

Responsibilities

Work directly with Enterprise Business Solutions program and product teams to identify highest-value use cases for agentic solutions, deployment, tool adoption, and infrastructure architecture
Partner with client-facing teams to understand customer use cases, ensuring conversational agents meet the needs of both end users and stakeholders.
Assist in the building of end-to-end agentic products and solutions (including multi-agent orchestration, tool use, and structured reasoning), from concept and design through implementation, ensuring scalability, adaptability, and alignment with business objectives.
Co-develop agentic solutions with business teams — enabling them to customize, maintain, and operate agents after initial deployment.
Translate ambiguous business problems into well-scoped agentic product and solution requirements with clear success metrics.
Leverage data insights to continuously optimize AI products and business outcomes — measuring adoption, output quality, user journeys, time-to-insight, and other KPIs.
Own agent quality by implementing evaluation frameworks, monitoring production accuracy, and driving prompt iteration cycles based on eval results.
Create and present documentation to business partners, execute product demos and walk-throughs, and deliver AI-driven analytics capabilities that enable real-time, data-informed decision-making across the enterprise.