Sr. Product Manager, Enterprise AI Solutions

NBCUniversal•New York, NY

3d•$130,000 - $170,000•Hybrid

About The Position

NBCUniversal is seeking a Senior Product Manager for its Enterprise Business Solutions AI Product team. The role embeds with engineering, product, and business teams to identify high-value problems and rapidly prototype, deploy, and improve AI products that serve NBCUniversal businesses. The position owns the full lifecycle of agent products, from discovery through deployment and ongoing iteration, and builds intelligent workflows that automate program operations, surface insights from complex data, and help teams use agentic solutions. The work combines technical, design, education, and product thinking. The product manager will understand business needs, identify opportunities for AI, write code, and deliver solutions with measurable effects on employees’ experience and work effectiveness. The role partners closely with Enterprise Business Solutions and client-facing teams, as well as business stakeholders, to develop scalable solutions and enable teams to customize and operate agents after deployment. The position currently follows a hybrid schedule, requiring work from the office at least four days per week. The company may change in-office requirements. The role is eligible for company-sponsored benefits, including medical, dental, and vision insurance, 401(k), paid leave, tuition reimbursement, discounts, and other perks. Salary is $130,000–$170,000 per year and the position is bonus eligible.

Requirements

Bachelor’s degree in data analytics, computer science, or a related field.
At least 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.
Expertise developing and deploying large language models and retrieval-augmented generation architecture, including prompt engineering, tool use, and agent orchestration; NLP; machine learning algorithms; and big data analytics.
Analytical skills to identify user patterns, insights, and performance metrics.
Ability to gather and understand complex requirements and challenges across business levels.
Ability to explain complex technical issues to technical and non-technical audiences, define requirements, and iterate on solutions.
Hands-on experience building products on large data sets and enterprise data architectures that require systems integration, data governance, and security compliance.
Hands-on experience with AI platforms, cloud services, and analytics tools such as Python, Azure, AWS, or Google Cloud.
Expertise in SDLC processes, scoping, iteration planning, user story writing, and rollout management.

Nice To Haves

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

Responsibilities

Work with Enterprise Business Solutions program and product teams to identify high-value use cases for agentic solutions, deployment, tool adoption, and infrastructure architecture.
Partner with client-facing teams to understand customer use cases and ensure conversational agents meet end-user and stakeholder needs.
Help build end-to-end agentic products and solutions, including multi-agent orchestration, tool use, and structured reasoning, from concept and design through implementation.
Ensure solutions are scalable, adaptable, and aligned with business objectives.
Co-develop agentic solutions with business teams and enable them to customize, maintain, and operate agents after deployment.
Translate ambiguous business problems into well-scoped product and solution requirements with clear success metrics.
Use data insights to 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 based on evaluation results.
Create and present documentation to business partners and conduct product demonstrations and walkthroughs.
Deliver AI-driven analytics capabilities that support real-time, data-informed decision-making across the enterprise.