AI/RAG engineer✦ anywhere

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.

Originally posted on Himalayas