Role & responsibilities

Responsibilities

- Build end-to-end RAG pipelines: parse chunk embed retrieve generate

- Develop agentic workflows using AgenticX, LangGraph, or custom orchestration

- Parse contract PDFs using layout-aware and OCR-based parsers (Unstructured, PyMuPDF, PDFPlumber)

- Enable multimodal processing (text + images/tables) using models like GPT-4V, LLaVA, Donut

- Integrate open-source models (Mistral, LLaMA3, Ollama) and vector databases (Milvus, FAISS, Qdrant)

- Build modular APIs (FastAPI) for Q&A;, summarization, and classification across documents

- Optimize retrieval/generation for long-context and multi-document inputs

Required Skills

- 6+ years in backend/ML; 1+ year in GenAI, RAG, or agentic architectures

- Robust Python skills;

experience with LangChain, LlamaIndex, Haystack

- Deep experience in PDF parsing (text, tables, images) and chunking strategies

- Familiarity with multimodal models (e.g., GPT-4V, LLaVA, or equivalents)

- Hands-on with vector DBs, FastAPI, Redis/Postgres

- Understanding of legal/contract structure and NLP challenges

📌 Gen Ai developer (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru