Role & responsibilities

- Design, develop, and maintain end-to-end Generative AI and Retrieval-Augmented Generation (RAG) solutions for contract document analysis.

- Build scalable document processing pipelines for parsing, chunking, embedding, retrieval, and response generation.

- Develop agentic AI workflows using Lang Graph, Agentic X, or similar orchestration frameworks.

- Implement multimodal AI capabilities to process text, images, tables, and complex document layouts.

- Integrate and fine-tune open-source LLMs such as Llama, Mistral, and Ollama-based models.

- Design and optimize vector search solutions using FAISS, Milvus, Qdrant, or similar vector databases.

- Work with OCR and document parsing frameworks such as Unstructured, Py MuPDF, and PDF Plumber to extract structured insights from contracts.

- Optimize retrieval accuracy, prompt engineering, and response quality for large-scale enterprise applications.

- Work with OCR and document parsing frameworks such as Unstructured, Py MuPDF, and PDF Plumber to extract structured insights from contracts.

- Collaborate with product, data science, and engineering teams to deliver AI-powered document intelligence solutions.

- Ensure scalability, performance, security, and maintainability of AI systems in production environments.

- Stay updated with the latest advancements in LLMs, Agentic AI, RAG architectures, and multimodal AI technologies.

Preferred candidate profile

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.





- 5+ years of experience in backend development, AI/ML engineering, or enterprise software development.

- At least 1 year of hands-on experience building Generative AI, RAG, LLM, or Agentic AI solutions.

- Strong expertise in Python and AI frameworks such as LangChain, LlamaIndex, Haystack, and LangGraph.

- Proven experience designing and deploying Retrieval-Augmented Generation (RAG) applications in production environments.

- Hands-on experience with vector databases such as FAISS, Qdrant, Milvus, Pinecone, or Weaviate.

- Experience working with open-source LLMs, including Llama, Mistral, and Ollama-based deployments.

- Strong understanding of document intelligence, PDF parsing, OCR, chunking strategies, embeddings, and semantic search.

- Experience developing scalable APIs and microservices using FastAPI and integrating AI services into enterprise applications.

- Familiarity with multimodal AI models capable of processing text, images, tables, and complex document layouts.

- Knowledge of cloud platforms such as Azure, AWS, or GCP and containerized deployments using Docker and Kubernetes.

- Understanding of prompt engineering, model evaluation, fine-tuning, and optimization techniques.

- Experience in legal technology, contract lifecycle management, compliance automation, or document analytics will be an advantage.

- Strong analytical, problem-solving, and communication skills with the ability to collaborate in cross-functional teams.

- Self-motivated professional who can work independently and deliver high-quality AI solutions in a rapid-paced environment.

📌 Walk-in || Senior Generative AI Engineer - RAG, Lang Graph & Multimodal AI (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru