We are seeking a Product Manager who will define and evolve the generative and agentic services that power our enterprise-grade platform for building AI applications. You will own the productization of generative and agentic services coming out of Pryon Research.

The Product Manager, Generative will drive benchmarking strategies to evaluate model quality, latency, and safety across both SaaS and on-prem deployments, ensuring our generative capabilities remain reliable and aligned with customer expectations. You will stay ahead of the rapidly shifting generative AI landscape by synthesizing market research, competitive insights, and customer feedback to guide roadmap direction. You will also shape how Pryon leverages function calling and the Model Context Protocol (MCP) to enable more robust and controlled agentic workflows.

In This Role, You Will:

Own the NL2SQL initiative in close partnership with Research, laying the foundation for how structured data is ingested, productized, and scaled across the platform.
Ensure core generative and RAG services are portable-ready, scalable, and designed to operate across deployment environments.
Partner closely with our orchestration tool to ensure seamless compatibility, and extensibility across agent workflows.
Define how we operationalize MCP (Model Context Protocol) to enable reliable, composable agentic workflows.
Own the health, reliability, and evolution of our underlying RAG and generative services, including quality, performance, and long-term extensibility.
Act as the connective tissue across Research, Platform, and Applications to unblock and accelerate customer-facing AI experiences.
Provide clear ownership and direction for platform capabilities that all downstream AI products and customer experiences depend on.
Lead agile ceremonies including refinement, roadmap reviews, sprint planning, and stand-ups.
Ensure on-time delivery of product features, enhancements and improvements.
Define use cases, set requirements and acceptance criteria for features, utilizing an iterative approach to allow for measurement of success and adoption, and prioritization of enhancements.
Own your roadmap and set prioritization for feature delivery from technical teams by synthesizing market research, customer and prospect data, customer needs, and internal/external stakeholder expectations.
Demonstrate full knowledge of the development process in your area of focus and at times be a key contributor and mindful guide to engineers and scientists on the team.
Interact with customers and market thought leaders to develop a deep understanding and define product requirements and best practices for an enterprise business problem.
Author product specifications, inclusive of use cases, and activity flow diagrams; identify data sources, analytics, reporting, and integration requirements.
Collaborate with engineering, data science, and other support teams such as user experience, QA, and documentation to create exceptional solutions.

What You Need to Be Successful:

Bachelor's degree or equivalent practical experience.
3-5 years of experience in product management or a related technical role.
2 years of experience developing or launching products or technologies within artificial intelligence or Machine Learning (AI or ML).
Exceptional written and verbal communication skills, with the ability to share technical knowledge and information across various audiences.
Demonstrated experience leading product, enterprise, or consumer communications at technology companies, including building communications functions or strategies from the ground up.

Preferred Qualifications:

Experience with coding/engineering fundamentals.
Deep knowledge of machine learning, artificial intelligence, and GenAI, with the ability to provide technical guidance to developers.
Proven experience in Product-Led Growth (PLG) initiatives and defining pricing/packaging strategies for technical products.
Prior experience engaging with or marketing to Enterprise and/or Federal customers or other highly regulated environments.
Intuitive understanding of how different technical and business audiences discover, evaluate, and adopt AI tools.