Product Owner, AI Factory

Our culture lifts you up—there is no ego in the way. Our common purpose? We all want to win for our customers. We aim to always be evolving, dynamic, and ambitious. We believe in the power of genuine connections. Each employee is a part of what makes us unique on the market: agile and dedicated. Time Type: Regular Job Description : Product Owner, AI Factory POSITION SUMMARY The Product Owner (PO) – AI Factory Squad is a pivotal technical product leader responsible for defining, industrializing, and executing the product vision and roadmap for the enterprise AI Factory platform. Operating within the Enterprise IT & Architecture Crew, this role drives the operational backbone (MLOps, LLMOps, AgentOps) that enables cross-functional business squads to repeatedly, safely, and cost-effectively build, deploy, and scale high-value AI models and Agentic AI solutions. Acting as the central bridge between enterprise architecture, data science, engineering, and business stakeholders, the Product Owner establishes an end-to-end platform blueprint. The PO champions self-serve capabilities, reusable components (e.g., Model Context Protocol [MCP] frameworks, tool belts, API gateways, certified skill libraries, semantic knowledge graphs), and automated CI/CD deployment pipelines to compress AI delivery time from months to days. Furthermore, the role enforces enterprise AI governance, Trust & Safety standards, AISecOps, and token unit economics (FinOps) to ensure all AI initiatives are scalable, secure, compliant, and directly tied to measurable business outcomes. KEY RESPONSABILITIES 1. Product Vision, Strategy & Roadmap Execution Define, prioritize, and execute the product vision and multi-quarter roadmap for the AI Factory Platform, serving as the central hub for the enterprise Agentic AI hub-and-spoke model. Own and manage the squad backlog, translating architectural guidelines, enterprise strategic goals, and cross-squad AI requirements into clear user stories, acceptance criteria, and technical deliverables. Deliver self-serve platform capabilities (such as the Service Catalog) to accelerate onboarding across internal squads and foster democratization of AI development. 2. MLOps, LLMOps & Agentic Operations (AgentOps) Industrialization Drive the industrialization of end-to-end automated CI/CD pipelines for AI models and autonomous agents, targeting rapid code-commit-to-production cycles . Lead the product roadmap for advanced multi-agent orchestration, arbitration platforms, tracing tools, and automated evaluation frameworks using Golden Datasets. Standardize and catalogue reusable platform assets, API tool belts, certified agent skill libraries, and semantic data/knowledge graph layers for widespread reuse across squads 3. AI Governance, Trust & Safety, and Security (AISecOps) Embed lightweight, continuous architectural guardrails, data access controls, and compliance mechanisms (privacy, security, model risk)…