Product Manager - Dallas, TX

Photon Career Site

About The Position

We are looking for a visionary Product Manager to lead the strategy and development of Agentic AI products for our clients. Unlike traditional PM roles, you will be responsible for defining products where the "user interface" is often an autonomous agent capable of reasoning, planning, and executing tasks. You will bridge the gap between complex AI research and tangible business value, ensuring that the agents we build are not just "smart," but reliable, ethical, and perfectly aligned with the customer's operational needs.

Requirements

Experience: 10+ years in Product Management, with at least 1–2 years of hands-on experience shipping LLM-based or Agentic products.
Technical Depth: Strong understanding of the Agentic stack: LLM reasoning (Chain of Thought), orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen), and vector databases.
Data-Driven Mindset: Ability to analyze model performance logs and translate technical failure points into product improvements.
Strategic Thinking: Experience in "Build vs. Buy" decisions regarding foundational models and agent frameworks.
Communication: Proven ability to explain complex concepts (like "temperature," "embeddings," or "latency vs. reasoning") to non-technical business executives.

Nice To Haves

Experience with multi-agent orchestration (assigning different tasks to different specialized agents).
Background in Computer Science, Cognitive Science, or a related field.
Knowledge of AI safety, ethics, and data privacy regulations (GDPR, SOC2) as they apply to autonomous agents.

Responsibilities

Product Vision & Strategy: Define the roadmap for agentic workflows, moving from simple prompt-response interactions to complex, multi-agent autonomous systems.
Defining "Agentic" Requirements: Identify use cases where AI agents can provide the most ROI—such as automated research, proactive customer support, or autonomous data analysis.
Evaluation & Benchmarking: Design and oversee "Eval" frameworks. Since AI is probabilistic, you will define what "success" looks like for an agent (e.g., accuracy, tool-call reliability, and task completion rates).
User Experience (UX) for AI: Reimagining the interface between humans and agents. You will define how users provide "human-in-the-loop" feedback and how agents should explain their reasoning.
Stakeholder & Client Management: Educate customers on the capabilities and limitations of Agentic AI. Manage expectations regarding the "black box" nature of LLMs and the iterative path to production-grade reliability.
Cross-functional Leadership: Collaborate closely with AI Engineers, Data Scientists, and Designers to prioritize features like RAG (Retrieval-Augmented Generation), memory persistence, and tool-use (function calling).