AI Engineer(Immediate Joiner)

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

Location: Gurugram/Bangalore

Experience: 35 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

Cloud/Infra: AWS (S3, Lambda, SageMaker), GCP, or Azure AI