Build production RAG pipelines. over large, messy engineering document corpora (PDFs with tables, specifications, drawings, and scanned appendices), including layout- aware ingestion, structure-aware chunking, hybrid retrieval, and reranking.
Engineer long-form structured generation. that produces multi-section engineering reports reliably, with deterministic document assembly, cross-section consistency, and numeric fidelity enforced through structured extraction rather than free-text generation.
Implement source traceability and grounding. so every generated claim carries citations back to source documents, with grounding checks that flag unsupported content before it reaches a human reviewer.
Design and operate agentic workflows. using Bedrock AgentCore and Step Functions, with bounded tool use, least-privilege per-session IAM, structured-output validation,



and defense against prompt injection from untrusted document content.
Keywords AWS Bedrock, Generative AI, RAG, LLM, Python, LangChain/LlamaIndex, Vector Databases (OpenSearch/Pinecone), AWS services (S3, Lambda, IAM, Step Functions), and production experience building AI/Agentic workflows.
Mandatory Key Skills (atleast 1) AWS Bedrock, Generative AI, RAG, LLM, Python, LangChain/LlamaIndex, Vector Databases (OpenSearch/Pinecone), AWS services (S3, Lambda, IAM, Step Functions), and production experience building AI/Agentic workflows.
Work Experience Required 5+ Year

Location - Mumbai, Delhi / NCR, Bengaluru , Kolkata, Chennai, Hyderabad, Ahmedabad, Pune,Remote

📌 Aws Data Engineer Mumbai
🏢 Serendipity corporate services
📍 Mumbai