AI RAG Engineer

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
  
  
  

• 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