AI/RAG engineer

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    "description": "<p>Job Responsibilities</p>\n<ol>\n<li>Building AI search agents- including ReAct, planning, and multi-agent architectures via custom implementation or frameworks like LangGraph, Dify, or CrewAI.</li>\n<li>Building end-to-end RAG pipelines from ingestion, chunking, embeddings, and hybrid vector search, ideally using Opensearch. </li>\n<li>Operating and monitoring vector/hybrid indexes (e.g. OpenSearch) in production environments.</li>\n<li>Implement grounding and citation to link generated answers back to their exact source passages. </li>\n<li>Automate evaluation using synthetic QA, retrieval-hit-rate tracking, and model-critique loops to continuously measure accuracy and detect drift.</li>\n<li>Orchestrating external tools or knowledge bases and monitoring latency and cost at production scale. </li>\n</ol>\n<p><br></p>\n<p>Qualifications</p>\n<ol>\n<li>Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.</li>\n<li>3+ years of experience in developing AI systems, with a focus on retrieval-augmented generation (RAG).</li>\n<li>Proven track record in building and optimizing end-to-end RAG pipelines. </li>\n<li>Experience with AI search agent development using frameworks like ReAct, LangGraph, Dify, or CrewAI. </li>\n<li>Hands-on experience with OpenSearch or similar vector search technologies. </li>\n<li>Proficiency in Python and relevant machine learning frameworks (e.g., PyTorch, TensorFlow). </li>\n<li>Strong understanding of data ingestion, chunking, embeddings, and hybrid vector search techniques. </li>\n<li>Experience with monitoring and managing production environments. </li>\n<li>Knowledge of grounding and citation techniques in AI-generated content. </li>\n<li>Familiarity with synthetic QA datasets and evaluation metrics.</li>\n</ol>",
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