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Youll work across Python services, large language models, retrieval workflows, prompt design, vector search, and agent-based integrations.\n\nWhat youll do:\n\n- Build GenAI applications using Python and established LLM and GenAI frameworks.\n\n- Design retrieval-augmented generation (RAG) workflows, including document processing, chunking, embeddings, retrieval, and response generation.\n\n- Develop and test prompts, system instructions, evaluation sets, and guardrails for business use cases.\n\n- Work with vector databases and hybrid search approaches to improve grounding and response quality.\n\n- Build agents that can call tools, use APIs, follow permissions, and complete defined tasks safely.\n\n- Create API services and application components that connect GenAI workflows to product interfaces.\n\n- Measure response quality, latency, cost, and failure cases, then improve the system through testing and iteration.\n\nWhat youll bring:\n\n- 4 - 5 years of relevant software, data, 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