AI Engineer-Immediate Joiner

Job Title: AI Engineer Python | RAG | LLM | Chunking | Vector DB

Location: Gurugram/Bangalore

Experience: 3–5 years

Employment Type: Full-Time

Job Summary

We are looking for an AI Engineer with deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and Vector Database architectures. The candidate should be skilled in Python-based AI pipelines, document chunking and embeddings, and model fine-tuning/integration for production-grade intelligent systems.

• The role involves building end-to-end AI-driven solutions that enhance knowledge retrieval, automate reasoning, and deliver scalable conversational or cognitive experiences.
• Key Responsibilities
• RAG Architecture Design:
• Develop and implement Retrieval-Augmented Generation pipelines using LLMs integrated with external knowledge sources and vector stores.
• LLM Integration & Fine-Tuning:
• Fine-tune or prompt-engineer models like GPT, Llama, Falcon, Mistral, T5, or Claude.
• Optimize inference workflows for efficiency, context management, and accuracy.
• Document Processing & Chunking:
• Design intelligent text-splitting and chunking strategies for long documents.
• Build embedding generation and context retrieval pipelines.
• Vector Database Management:
• Integrate and optimize vector stores like FAISS, Pinecone, Chroma, Weaviate, Milvus, or Qdrant.
• Implement similarity search, hybrid retrieval, and ranking mechanisms.
• Python-Based AI Development:
• Build APIs and microservices using FastAPI / Flask / LangChain / LlamaIndex.
• Create reusable AI pipelines for inference, retraining, and data ingestion.
• Data Handling & Preprocessing:
• Clean, transform, and index structured and unstructured data for efficient knowledge retrieval.
• Performance Optimization & Monitoring:
• Evaluate model performance using precision, recall, BLEU, ROUGE, or RAG-specific metrics.
• Deploy and monitor models using Docker, MLflow, and cloud environments (AWS/GCP/Azure).
• Collaboration:
• Work cross-functionally with data scientists, backend engineers, and domain experts to integrate AI models into enterprise applications.
• Required Skills & Tools
• Core Skills
• Programming: Python (mandatory), familiarity with TypeScript or Node.js is a plus
• LLM Frameworks: LangChain, LlamaIndex, Hugging Face Transformers
• Vector Databases: FAISS, Pinecone, Chroma, Weaviate, Milvus, Qdrant
• Model Types: OpenAI GPT, Llama2/3, Mistral, Falcon, Claude, Gemini
• Embedding Models: Sentence Transformers, OpenAI Embeddings, Instructor, or Custom Models
• RAG Stack: Document loaders, text chunking, embedding generation, retrieval, context assembly
• APIs & Deployment: FastAPI, Flask, Docker, MLflow, Streamlit
• Version Control: Git, GitHub/GitLab