PO - AI Assistant Platform

1. Product Strategy & Vision Define the product roadmap for the AI Assistant Platform based on business goals and technology readiness. Translate technical possibilities (LLM, vector search, RAG, Kafka, etc.) into user-centric use cases. Collaborate with leadership to align business strategy with platform scalability and multi-domain deployment. 2. Requirements & Backlog Management Gather requirements from stakeholders, internal users, and integration partners. Create detailed User Stories / Acceptance Criteria in Jira or Notion. Prioritize backlog items for both MVP and subsequent releases based on impact and complexity. Maintain documentation of API workflows, data ingestion flows, and user interaction models. 3. Coordination & Delivery Work closely with Tech Lead, BA, and Engineering to ensure clarity of goals and technical feasibility. Lead sprint planning, backlog grooming, and review sessions. Ensure deliverables align with defined acceptance criteria and SLO targets (latency, recall@k, etc.). Manage integration milestones across modules: Gateway, Orchestrator, VectorDB, LLM, and UI dashboard. 4. Product Quality & Data Feedback Validate AI accuracy, semantic precision, and UX through feedback loops and evaluation metrics. Define measurable KPIs for model response quality, latency, and user satisfaction. Collaborate with QA on testing strategy for chat interactions, embeddings, and retrieval outputs. 5. Cross-Team & Stakeholder Collaboration Serve as the bridge between technical and non-technical teams (business, data, and ops). Communicate product progress and risks clearly to management and stakeholders. Support onboarding of new tenants or domain connectors (IFRS, Football, ERP, etc.).