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9 days ago
Requirements and responsibilities
Roles & ResponsibilitiesOwn end-to-end product execution across knowledge ingestion, retrieval quality, grounded answer generation, enterprise chat experience, guardrails, observability, and agentic use case rollout.Own quality of the RAG pipeline by partnering with technical teams on ingestion readiness, chunking/indexing implications, retrieval tuning, grounding, citation behavior, and answer-quality improvements.Define and own the evaluation framework, including metrics for retrieval relevance, answer correctness, faithfulness, hallucination rate, citation accuracy, fallback handling, and latency — ensuring every test run is measured against established baselines.Maintain product-level feedback loops using golden datasets, user feedback, UAT findings, and post-release learning to continuously improve quality.Support rollout of agentic automation use cases for lower-complexity workflows, while ensuring proper safety controls, approval flows, and auditability are in place.SkillsGenAI / RAG SkillsExperience building and shipping enterprise-grade GenAI products, especially RAG-based assistants or enterprise knowledge assistants.Understanding of the end-to-end RAG lifecycle, including data ingestion, document parsing, chunking, metadata enrichment, embeddings, indexing, retrieval, grounding, citation, and response synthesis.Familiarity with cloud-based GenAI architecture, including managed LLM services, vector/search indexes, object storage, workflow orchestration, serverless compute, containerized services, and enterprise integration patterns.Understanding of observability and monitoring for LLM products, including trace-level visibility, query lifecycle analysis, latency, token usage, drift, alerting, rollback, and user feedback instrumentation.Product Management SkillsStrong execution and delivery management skills, with the ability to drive a defined roadmap across multiple technical teams and dependencies.Experience writing clear requirements, acceptance criteria, and release scope for technically complex products.Experience owning product validation and testing before release, with clear quality bars and definition of done.Excellent stakeholder management skills across business users, end-user teams, engineering, platform, security, and leadership.Strong communication skills, including ability to explain AI quality, trust, retrieval issues, and guardrails in business-friendly language.
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