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# RAG Engineer

- **Company:** Sumeru Global Technologies Private Limited
- **Location:** Jersey City, NJ, United States
- **Experience:** Experienced
- **Salary:** $120,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Microsoft Azure, Cloud Storage, Encodings, Databases, Continuous Integration, Information Engineering, Data Governance, Data Security, Elasticsearch, Graph Database, Python (Programming Language), Node.Js, Parsing, Role-Based Access Control, Standard Sql, Search Technologies, Management of Software Versions, Indexer, Backend, Search Engines
- **Published:** June 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=70d6e8b34b09205c

## About the Role

Do you have experience in Search engines?, · Strong backend/data engineering using Python, Java, Node.js, or .NET. Strong SQL and database experience.

· Minimum 2 years hands-on AI/RAG implementation experience.

· Experience with embeddings, vector search, hybrid search, document ingestion, and RAG pipelines. Experience with Azure AI Search, pgvector, Pinecone, Weaviate, Qdrant, Milvus, Elasticsearch, OpenSearch, or equivalent.

· Understanding of chunking, retrieval tuning, reranking, context assembly, citations, and retrieval evaluation. BFSI, banking, insurance, capital markets, payments, or regulated enterprise experience. Client implementation experience.

· Preferred Skills

· Azure Blob Storage, Azure AI Search, Azure Document Intelligence, Azure OpenAI, or similar Azure services.

· Experience with data governance, document lineage, metadata models, knowledge graphs, or ontology-driven retrieval.

· Experience with CI/CD, containers, observability, and secure cloud deployment

## Description

· Build document ingestion, parsing, chunking, metadata extraction, embedding, indexing, and versioning pipelines.

· Implement RAG services using vector search, keyword search, hybrid retrieval, metadata filters, reranking, and citations.

· Integrate with Azure AI Search, pgvector, Pinecone, Weaviate, Qdrant, Milvus, Elasticsearch, OpenSearch, or similar platforms.

· Build APIs for retrieval, source traceability, ingestion status, feedback capture, and citation support. Implement permission-aware retrieval, RBAC, tenant isolation, audit logging, PII handling, and secure data access.

· Support retrieval quality evaluation, grounding, hallucination reduction, client demos, UAT, and production readiness.


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