Senior AI Product Manager, Customer Engagement

webAI•Austin, TX

About The Position

We are looking for a Senior AI Product Manager to drive our Customer Engagement vertical. You will be bridging the gap between ambiguous customer problems and rigorous value delivery. While our research teams push the boundaries of distributed AI, your role is to ensure that when we engage with a customer, we build the right thing the first time. You will own the discovery process that produces clarity, allowing our engineers to focus on building rather than guessing. You will define the "Evaluation Contract" before a single line of code is written and partner with AI engineers to select the best technical approach (e.g., Prompt Engineering vs. RAG vs. Fine-tuning) for the specific job-to-be-done. At the Senior level, you are not just executing discovery; you are defining how we scale the function. You will set the standard for "Problem Briefs" and "Evaluation Contracts" that the rest of the organization follows.

Requirements

5+ years of Technical Product Management or Solution Architecture experience.
2+ years specifically shipping AI, ML, Search, or Data products to production.
Deep Technical Fluency: You can explain the difference between vector search and keyword search, or why a specific prompt strategy is failing. You are comfortable reading technical papers or API docs.
Proven "Zero-to-One" Execution: Experience taking a product from a loose concept to a live deployment with real users.
Client-Facing Maturity: Experience navigating complex stakeholder landscapes; you can say "no" to a customer feature request by explaining the technical trade-offs clearly.
Data-Driven: Proficiency with evaluation frameworks; experience defining "ground truth" datasets.

Responsibilities

Own the Discovery Process: Lead scoping to produce a Problem Brief for every engagement: the job-to-be-done, constraints, success criteria, and reliability expectations.
Define Evaluation Contracts: Define what data, metrics, and thresholds constitute success before building begins.
Partner on Technical Approach: Work with AI Engineers to determine the right tool for the job. You must be able to debate the trade-offs of RAG vs. Long-Context vs. Fine-tuning based on data, cost, and latency.
Extract & Standardize: Identify patterns in customer needs to surface platform gaps early; turn field learnings into engineering requirements.
Enable Focus: Your primary metric is clarity—reducing ambiguity so AI engineers can execute immediately without churning on requirements.
Synthesize Market Patterns: Look across multiple customer deployments to identify strategic platform gaps. You don't just report bugs; you propose architectural improvements to the core platform based on field evidence.