Global Technical Product Owner – Enterprise AI, Data & Cybersecurity
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Key skills for this role
Role Overview
Lead the strategy and delivery of enterprise AI solutions across data management, Generative AI, technology operations, and cybersecurity.
This is a permanent, onsite position in Abu Dhabi, and relocation is required.
Key Skills for This Role
Full Job Posting
About the Job
Lead the strategy and delivery of enterprise AI solutions across data management, Generative AI, technology operations, and cybersecurity.
This is a permanent, onsite position in Abu Dhabi, and relocation is required.
Responsibilities
- Own the enterprise AI product strategy, roadmap, and portfolio across data management, Generative AI, technology operations, and cybersecurity.
- Translate business and technical requirements into scalable AI products, platforms, and enterprise agents.
- Lead AI-enabled data engineering, automation, data quality, metadata, lineage, and governance initiatives.
- Drive Generative AI and unstructured-data solutions using LLMs, RAG, GraphRAG, vector databases, and agentic AI.
- Establish AI evaluation frameworks, guardrails, observability, and responsible-use standards.
- Lead cross-functional squads and manage products from pilot through production, adoption, and lifecycle optimization.
Qualifications
- At least 8 years of experience across AI product management, data engineering, enterprise technology, or cybersecurity transformation.
- Proven delivery experience with enterprise AI, Generative AI, or agentic solutions.
- Strong product roadmap, Agile delivery, and cross-functional leadership experience.
- Knowledge of cloud data platforms, AI governance, data quality, and cybersecurity controls.
- Experience with cloud-native data engineering and data pipeline modernization.
- Banking or regulated-industry experience is preferred.
Required Skills
- Enterprise AI or Generative AI product strategy and roadmap ownership.
- Technical product management and multi-squad Agile delivery.
- LLMs, RAG, GraphRAG, agentic AI, and AI evaluation frameworks.
- Cloud-native data engineering and data pipeline modernization.
- Metadata, lineage, data quality, governance, and sensitive-data controls.
- Technology operations, software engineering, or cybersecurity transformation.
- Senior stakeholder management and cross-functional leadership.
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