Senior Technical AI Product Owner

To lead the delivery of AI-driven product solutions by translating strategic vision into actionable requirements and prototypes. The Senior Technical AI Product Owner collaborates with Product Managers, UX, engineering teams, and data scientists to define scope, feasibility, and success metrics for AI initiatives. This role ensures operational readiness, governance, and compliance while driving continuous improvement of AI models and workflows. With deep technical expertise and a strong understanding of user needs, the position focuses on delivering scalable, secure, and high-value AI experiences that align with business objectives. 

• Collaborate with Product Managers to understand the market demands, priorities, and overall product strategy  

• Partner with UX and Product Managers on market research efforts to understand and prioritize customer needs  

• Work with one or more engineering teams to deliver timely, quality releases that provide high business value and exceed customer expectations  

• Flexibility to support more than one product within a portfolio, potentially across value streams  

• Translate product strategy into detailed requirements and prototypes  

• Work with the Product Manager and Agile team to define the scope included in a release.  

• Understand user personas and partner with UX to develop  

AI intake, discovery & feasibility (prototype-first execution) 

• Run structured intakes with product teams to translate ‘AI requests’ into well-scoped problem statements, hypotheses and phased plans 

• Define feasibility gates (data access/quality, workflow integration, security/compliance readiness, evaluation approach) 

• Drive prototype outcomes: what was tested, how it was measured, what worked/failed and recommendation to proceed / pivot / stop 

Requirements & acceptance criteria for AI experiences 

• Translate product workflows into AI-ready requirements: inputs/outputs, confidence thresholds, fallback behavior, error handling and user experience expectations 

• Define non-functional requirements: latency, cost considerations, scalability, reliability and observability 

• Ensure required integration hooks are delivered by partner teams (API contracts, UI entry points, telemetry, feedback capture) 

Model quality, measurement & operational readiness 

• Partner with data science to define evaluation strategy and success metrics (accuracy/precision/recall, coverage, confidence calibration) 

• Ensure regression testing is in place for model/prompt changes and releases don’t degrade performance 
• • Drive continuous improvement loops: error taxonomy, labeling needs, retraining/refresh planning and iterative releases 
  • Ensure production readiness: monitoring dashboards, alerting, runbooks, incident triage paths and ownership clarity 

  Governance, privacy, security & auditability (healthcare-grade) 

  • Ensure PHI/PII handling, tenant isolation, least-privilege access and retention requirements are built into scope and acceptance criteria 
  • Coordinate with Security on guardrails such as content filtering, safe failure behavior and prompt/data handling constraints 
  • Ensure traceability: output metadata, model/prompt/versioning, decision logs and audit-friendly event logging where required 

  Platform alignment & reuse across products 

  • Coordinate dependencies with Platform Service teams as needed 
  • Promote reuse of shared AI components (evaluation harnesses, prompt templates/standards, monitoring patterns, common pipelines) to reduce duplicated effort across products 
  • Use metrics and feedback to improve team and organization processes, best practices, performance, and delivery  
  • May provide work direction or guidance to colleagues with less experience  
  • Act as a product champion within the company  

• 6+ years of product management experience delivering enterprise SaaS products 

• Demonstrated experience delivering ML/AI-enabled product capabilities (predictive models, NLP/document intelligence, recommendations, optimization, anomaly detection) 

• Strong product discovery & requirements skills: customer research, PRDs, roadmap planning, prioritization & stakeholder alignment 

• Working knowledge of ML/AI evaluation & lifecycle management: metrics, offline testing, human review loops, monitoring signals & iterative improvement 

• Strong cross-functional leadership across engineering, data science, data engineering & security stakeholders 

Skills Required: 

• Outcome-driven product thinking and comfort operating in ambiguity 

• Excellent written communication (PRDs, customer narratives, exec-ready updates) 

• Metrics-first mindset connecting model performance to user outcomes and business impact 

• Pragmatic governance - ships safely without stalling delivery 

• Strong judgment on trade-offs: quality vs. speed, cost vs. benefit, generalization vs. customization 

Personal Attributes: 

• Analytical mindset with strong problem-solving capabilities. 

• Detail-oriented with a commitment to data quality and accuracy. 

• Self-driven and able to work independently while fostering collaboration. 

• Team-oriented with a focus on stakeholder satisfaction and business impact.