Key skills
Technical Product ManagementAI/ML Product ShippingTechnical FluencyZero-to-One ExecutionData-Driven EvaluationClient-Facing MaturityStakeholder ManagementProblem SolvingCommunication SkillsAIArtificial IntelligenceMLProduct Management
About this role
webAI is pioneering the future of artificial intelligence with a focus on distributed AI infrastructure. They are seeking a Senior AI Product Manager to lead the Customer Engagement vertical, ensuring the delivery of valuable solutions by bridging customer needs with technical execution.
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
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