Senior Generative Engineer (RAG & LLM)

We're Hiring: Generative AI Engineer (RAG & LLMs) Location: Gururgram Experience: 26 Years Employment Type: Full-Time Are you passionate about building next-generation AI applications We're looking for a Senior Generative AI Engineer with hands-on experience in RAG (Retrieval-Augmented Generation) , chunking strategies , retrieval optimization , and LLM-based AI solutions . Key Responsibilities Design and develop enterprise-grade GenAI applications using Large Language Models (LLMs). Build and optimize RAG pipelines for intelligent document retrieval and question answering. Implement advanced chunking strategies , indexing techniques, and retrieval optimization for improved AI accuracy. Develop AI agents, chatbots, and knowledge assistants using LangChain, LlamaIndex, or similar frameworks. Work with vector databases such as Pinecone, FAISS, ChromaDB, Weaviate, or Milvus. Integrate LLMs including OpenAI GPT, Claude, Gemini, Llama, and Mistral. Optimize prompts, context windows, embeddings, and response quality. Build scalable REST APIs using Python (FastAPI/Flask). Collaborate with cross-functional teams to deploy production-ready AI solutions. Required Skills 26 years of experience in Python development and Generative AI. Strong hands-on experience with: Retrieval-Augmented Generation (RAG) Chunking Strategies (Semantic, Recursive, Token-based, Hybrid) Embedding Models & Vector Search LangChain / LlamaIndex Prompt Engineering OpenAI, Claude, Gemini, Llama, or similar LLMs Vector Databases (Pinecone, ChromaDB, FAISS, Weaviate, Milvus) Good understanding of: Document parsing and knowledge retrieval Retrieval strategies (Hybrid Search, Reranking, Metadata Filtering) FastAPI / Flask REST APIs SQL / NoSQL databases Git and Docker Preferred Skills Experience with Speech-to-Text (Whisper, Deepgram) and Text-to-Speech (ElevenLabs, Azure TTS). Knowledge of AI Agents, MCP (Model Context Protocol), and Agentic AI. Experience with AWS, Azure, or GCP. Familiarity with Hugging Face, Transformers, and open-source LLMs. Exposure to Kubernetes and production deployment of AI applications. Why Join Us Work on cutting-edge AI and LLM-based products. Build scalable enterprise AI solutions. Collaborate with a highly skilled engineering team. Competitive compensation and excellent growth opportunities. We're Hiring: Generative AI Engineer (RAG & LLMs) Location: Gururgram Experience: 26 Years Employment Type: Full-Time Are you passionate about building next-generation AI applications We're looking for a Senior Generative AI Engineer with hands-on experience in RAG (Retrieval-Augmented Generation) , chunking strategies , retrieval optimization , and LLM-based AI solutions . Key Responsibilities Design and develop enterprise-grade GenAI applications using Large Language Models (LLMs). Build and optimize RAG pipelines for intelligent document retrieval and question answering. Implement advanced chunking strategies , indexing techniques, and retrieval optimization for improved AI accuracy. Develop AI agents, chatbots, and knowledge assistants using LangChain, LlamaIndex, or similar frameworks. Work with vector databases such as Pinecone, FAISS, ChromaDB, Weaviate, or Milvus. Integrate LLMs including OpenAI GPT, Claude, Gemini, Llama, and Mistral. Optimize prompts, context windows, embeddings, and response quality. Build scalable REST APIs using Python (FastAPI/Flask). Collaborate with cross-functional teams to deploy production-ready AI solutions. Required Skills 26 years of experience in Python development and Generative AI. Strong hands-on experience with: Retrieval-Augmented Generation (RAG) Chunking Strategies (Semantic, Recursive, Token-based, Hybrid) Embedding Models & Vector Search LangChain / LlamaIndex Prompt Engineering OpenAI, Claude, Gemini, Llama, or similar LLMs Vector Databases (Pinecone, ChromaDB, FAISS, Weaviate, Milvus) Good understanding of: Document parsing and knowledge retrieval Retrieval strategies (Hybrid Search, Reranking, Metadata Filtering) FastAPI / Flask REST APIs SQL / NoSQL databases Git and Docker Preferred Skills Experience with Speech-to-Text (Whisper, Deepgram) and Text-to-Speech (ElevenLabs, Azure TTS). Knowledge of AI Agents, MCP (Model Context Protocol), and Agentic AI. Experience with AWS, Azure, or GCP. Familiarity with Hugging Face, Transformers, and open-source LLMs. Exposure to Kubernetes and production deployment of AI applications. Why Join Us Work on cutting-edge AI and LLM-based products. Build scalable enterprise AI solutions. Collaborate with a highly skilled engineering team. Competitive compensation and excellent growth opportunities.