Technical Product Owner

Role Purpose
Own and drive the enterprise AI platform and foundational AI services roadmap, ensuring scalable, secure, and reusable AI capabilities are available across the organization.
This role acts as the bridge between Data Science, AI Engineering, Platform Engineering, Enterprise Architecture, and Business teams, translating AI requirements into reusable platform capabilities and technical solutions.
Initially, this role will operate as an Individual Contributor (IC) with end-to-end ownership of the AI platform foundational capabilities product roadmap and execution, with the opportunity to evolve into a people leadership role as the AI function scales.
Key Responsibilities
AI Platform Product Strategy & Ownership

• Define and own the roadmap for enterprise AI foundational services and platform capabilities.
• Identify, prioritize, and manage platform investments across Generative AI, Agentic AI, Model Services, AI Gateways, Vector Databases, RAG frameworks, Guardrails, Evaluation Frameworks, and Model Operations.
• Translate business and AI use case requirements into reusable platform capabilities and services.
• Define success metrics, adoption KPIs, platform SLAs, and value realization measures for AI platform services.
  Technical Solution Ownership & Standards
• Own the functional and technical requirements for enterprise AI platform capabilities.
• Partner with AI Engineering, Data Science, and Platform teams to define scalable solution architectures and implementation approaches.
• Establish standards for AI engineering, model lifecycle management, observability, evaluation, security, and operational readiness.
• Drive alignment with enterprise architecture, cloud strategy, data platforms, security, and governance standards.
• Review and guide technical solution designs to ensure scalability, reusability, maintainability, and compliance.
  Stakeholder & Executive Engagement
• Act as the primary product owner for enterprise AI platform capabilities.
• Engage with business, technology, and leadership stakeholders to understand AI adoption needs and platform priorities.
• Facilitate collaboration across Data Science, Engineering, Architecture, Infrastructure, Security, and Product teams.
  Ideal Candidate Profile
• 10–15+ years of experience in Product Management, AI Engineering, Data Engineering, Cloud Platforms, or related technology leadership roles.
• Proven experience delivering enterprise-scale AI platforms, AI engineering solutions, or cloud-native platforms.
• Working level understanding of:

Generative AI, LLMs, RAG, Agentic AI, and AI application architectures
2.
AI Engineering, MLOps, LLMOps, model evaluation, monitoring, and observability
3.
AI infrastructure including model serving, vector databases, orchestration frameworks, and AI gateways
4.
Enterprise data architecture, metadata, governance, and platform engineering principles
5.
Cloud-native AI platforms and services (AWS, Azure, GCP)

• Experience working closely with Data Science, Engineering, Architecture, and Product organizations.