Responsibilities
- Define and manage the strategic direction of the internal 'AI as a Platform' initiative, prioritizing scalability, reliability, and seamless integration across internal teams.
- Collaborate closely with domain and product teams to uncover opportunities where AI can enhance client experience and streamline operations.
- Lead the execution of key AI use cases company-wide, coordinating delivery timelines across AI, domain, and technical teams.
- Serve as the primary advocate for compliant, ethical, and secure AI practices, ensuring alignment with data protection regulations and regulatory standards.
- Oversee the end-to-end lifecycle of client-facing AI features, from planning through post-launch evaluation, in coordination with cross-functional stakeholders.
- Manage the product development lifecycle for the AI Platform team, including backlog prioritization, agile ceremonies, and roadmap execution.
- Convert strategic business objectives into detailed, actionable requirements for data science and machine learning engineering teams.
- Use data-driven insights, user feedback, market developments, and analysis of LLM and AI system outputs to refine the platform’s product vision and strategy.
Responsibilities
- Define and manage the strategic direction of the internal 'AI as a Platform' initiative, prioritizing scalability, reliability, and seamless integration across internal teams.
- Collaborate closely with domain and product teams to uncover opportunities where AI can enhance client experience and streamline operations.
- Lead the execution of key AI use cases company-wide, coordinating delivery timelines across AI, domain, and technical teams.
- Serve as the primary advocate for compliant, ethical, and secure AI practices, ensuring alignment with data protection regulations and regulatory standards.
- Oversee the end-to-end lifecycle of client-facing AI features, from planning through post-launch evaluation, in coordination with cross-functional stakeholders.
- Manage the product development lifecycle for the AI Platform team, including backlog prioritization, agile ceremonies, and roadmap execution.
- Convert strategic business objectives into detailed, actionable requirements for data science and machine learning engineering teams.
- Use data-driven insights, user feedback, market developments, and analysis of LLM and AI system outputs to refine the platform’s product vision and strategy.