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llms rag genai sagemaker bedrock

📋 Description

  • Own end-to-end GenAI/agentic AI product lifecycle in enterprise.
  • Define vision, strategy, and roadmap for GenAI agent workflows.
  • Translate enterprise problems into outcome-driven requirements.
  • Run experiments and pilots to validate agentic solutions.
  • Build reference architectures, playbooks, and patterns.
  • Partner with engineering on context management and deployment readiness.

🎯 Requirements

  • 8-12+ years in product mgmt or solutions engineering; shipped AI products.
  • Deep GenAI fluency: LLMs, RAG, fine-tuning; hands-on agentic systems.
  • Prototyping AI with tools (Cursor, Claude, Copilot) to validate hypotheses.
  • Enterprise AI deployment: read code, evaluate architectures, scale deployments.
  • Exceptional communicator; PRDs, specs, decisions; align with execs.
  • Comfortable in ambiguous, fast-moving AI landscape; AWS AI ecosystem fluency.
  • Software background (Python, JavaScript, TypeScript) preferred.
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