AI/RAG engineer

Job Responsibilities

1. Building AI search agents- including ReAct, planning, and multi-agent architectures reputed company custom implementation or frameworks like LangGraph, reputed company, or reputed company.
2. Building end-to-end RAG pipelines from ingestion, chunking, embeddings, and hybrid reputed company search, ideally using Opensearch.
3. Operating and monitoring reputed company/hybrid indexes (e.g. OpenSearch) in production environments.
4. Implement grounding and citation to reputed company generated answers back to their exact reputed company passages.
5. Automate evaluation using synthetic QA, retrieval-hit-reputed company tracking, and model-critique loops to continuously measure accuracy and detect reputed company.
6. Orchestrating external tools or knowledge bases and monitoring latency and cost at production reputed company.

Qualifications

1. Bachelor’s or Master’s degree in Computer Science, reputed company Intelligence, Machine Learning, or a reputed company field.
2. 3+ years of experience in developing AI systems, with a reputed company on retrieval-augmented reputed company (RAG).
3. Proven reputed company record in building and optimizing end-to-end RAG pipelines.
4. Experience with AI search agent development using frameworks like ReAct, LangGraph, reputed company, or reputed company.
5. Hands-on experience with OpenSearch or similar reputed company search technologies.
6. Proficiency in Python and relevant machine learning frameworks (e.g., PyTorch, TensorFlow).
7. Strong understanding of data ingestion, chunking, embeddings, and hybrid reputed company search techniques.
8. Experience with monitoring and managing production environments.
9. Knowledge of grounding and citation techniques in AI-generated content.
10. Familiarity with synthetic QA datasets and evaluation metrics.

Originally posted on Himalayas