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.

Originally posted on Himalayas

AI/RAG engineer at CoinMarketCap