AI/RAG Engineer

Stack

Responsibilities

• ▹Build AI search agents, including ReAct, planning and multi-agent architectures, via custom implementation or frameworks like LangGraph, Dify or CrewAI
• ▹Build end-to-end RAG pipelines from ingestion, chunking and embeddings to hybrid vector search, ideally using OpenSearch
• ▹Operate and monitor 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
• ▹Orchestrate external tools and knowledge bases and monitor latency and cost at production scale

Requirements

• ▹Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning or a related field
• ▹3+ years of experience developing AI systems, with a focus on retrieval-augmented generation (RAG)
• ▹Proven track record 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 monitoring and managing production environments
• ▹Knowledge of grounding and citation techniques in AI-generated content
• ▹Familiarity with synthetic QA datasets and evaluation metrics