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

AI/RAG engineer

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