The AI Enablement Lead is responsible for leading the delivery of AI-driven product solutions by translating strategic vision into actionable requirements, prototypes, and scalable production capabilities.

This role partners closely with Product Management, UX, Engineering, Data Science, Platform, and Security teams to ensure AI initiatives are feasible, governed, measurable, and aligned with business objectives.

The position plays a critical role in enabling secure, compliant, and high-impact AI experiences across products, ensuring operational readiness while continuously improving AI models, workflows, and delivery practices.

Key Responsibilities :

Product Strategy & AI Enablement : :

- Partner with Product Managers to understand market demands, customer needs, priorities, and overall product strategy Collaborate with UX and Product teams on customer research to identify and prioritize high-value AI use cases

- Translate product vision and strategy into clear AI-ready requirements, user stories, and prototypes

- Support multiple products within a portfolio, potentially across multiple value streams AI Intake

- Run structured AI intake sessions to translate business requests into well-defined problem statements, hypotheses, and phased execution plans

- Define feasibility gates including data access and quality, workflow integration, security and compliance readiness, and evaluation approach

- Drive prototype-first execution, documenting outcomes, metrics, learnings, and clear recommendations to proceed, pivot, or stop

Delivery & Release Management :

- Work with one or more Agile engineering teams to deliver timely, high-quality AI-enabled releases

- Partner with Product Managers and Agile teams to define release scope, priorities, and acceptance criteria

- Ensure AI features deliver measurable business value and exceed customer expectations AI

Requirements & Acceptance Criteria :

- Define AI-specific functional requirements including inputs, outputs, confidence thresholds, fallback behavior, error handling, and user experience expectations

- Define non-functional requirements such as latency, cost efficiency, scalability, reliability, and observability

- Ensure required integration hooks are delivered by partner teams including APIs, UI entry points, telemetry, and feedback mechanisms Model Quality, Measurement & Operational Readiness

- Partner with Data Science teams to define evaluation strategies and success metrics (accuracy, precision, recall, coverage, confidence calibration)

- Ensure regression testing is implemented to prevent performance degradation from model or prompt changes

- Drive continuous improvement through error analysis, labeling strategies, retraining plans, and iterative releases

- Ensure production readiness with monitoring dashboards, alerting, runbooks, incident triage processes, and clear ownership Governance, Privacy, Security & Compliance

- Ensure privacy, security, and compliance requirements are embedded into scope and acceptance criteria, including PHI/PII handling and data retention

- Coordinate with Security teams on guardrails such as content filtering, safe-failure behaviors, and prompt/data handling constraints

- Ensure auditability and traceability through metadata capture, model and prompt versioning, decision logs, and audit-friendly event logging

Platform Alignment & Reuse :

- Coordinate dependencies with platform and shared services teams

- Promote reuse of shared AI components, frameworks, evaluation tools, monitoring patterns, and pipelines to reduce duplication

- Leverage metrics and feedback to improve team practices, delivery efficiency, and organizational AI maturity

Leadership & Influence :

- Act as an AI product champion within the organization

- Provide guidance and direction to less-experienced team members when required Influence stakeholders through data-driven insights, strong judgment, and clear communication

Required Skills & Experience Experience :

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

- Proven experience delivering ML/AI-enabled product capabilities such as predictive models, NLP, document intelligence, recommendations, optimization, or anomaly detection

Technical & Product Skills :

- Strong product discovery and requirements expertise including customer research, PRDs, roadmaps, prioritization, and stakeholder alignment

- Working knowledge of ML/AI lifecycle management including evaluation metrics, offline testing, human-in-the-loop review, monitoring signals, and continuous improvement Metrics-driven mindset linking model performance to user outcomes and business impact

- Strong understanding of AI governance, security, privacy, and compliance considerations

- Ability to balance trade-offs across quality, speed, cost, scalability, and customization

Core Competencies :

- Outcome-driven product thinking with comfort operating in ambiguity

- Excellent written and verbal communication skills, including executive-level documentation and updates

- Strong cross-functional leadership across engineering, data science, data engineering, UX, and security teams

- Pragmatic approach to governance that enables safe delivery without slowing innovation