AI Product Manager

Role Summary We are seeking an AI Product Manager to be embedded within the AI Products Management Department. The successful candidate will lead the end-to-end lifecycle of AI-driven features, ensuring our AI Products are not just technically sound, but commercially viable and regulatory compliant. The AI Product Manager also bridges the gap between technical Data Scientists and AI Research team, and prospective clients and business stakeholders demands. Key Responsibilities • Strategy & Roadmap Define the product vision for AI initiatives, prioritizing features based on market needs, technical feasibility, and ROI. • Model Lifecycle Management Collaborate with Data Science and Research teams to guide model development from discovery and training to deployment and tuning. • Cross-Functional Leadership Serve as the "translator" between Engineering, Design, Legal, and Business stakeholders. • Performance Tracking Define and monitor success metrics, focusing on both traditional product KPIs and AI-specific metrics like precision, recall, and latency. • Privacy and Security Oversight Ensures data privacy, bias mitigation, and transparency in all algorithmic outputs. • AI Operations Specify MLOps/LLMOps requirements, e.g. model registry, CI/CD, prompt/config versioning, observability, monitoring, rollback strategies. Also, define runtime controls including guardrails, policy enforcement, red-teaming, rate limiting, and audit logging. Show more Show less

Define the product vision for AI initiatives, prioritizing features based on market needs, technical feasibility, and ROI. Collaborate with Data Science and Research teams to guide model development from discovery and training to deployment and tuning. Serve as the "translator" between Engineering, Design, Legal, and Business stakeholders. Define and monitor success metrics, focusing on both traditional product KPIs and AI-specific metrics like precision, recall, and latency. Ensures data privacy, bias mitigation, and transparency in all algorithmic outputs. Specify MLOps/LLMOps requirements, e.g. model registry, CI/CD, prompt/config versioning, observability, monitoring, rollback strategies. Also, define runtime controls including guardrails, policy enforcement, red-teaming, rate limiting, and audit logging.