Overview

RAG Engineer + AI Developer

Responsibilities

• Design, develop, and maintain Retrieval-Augmented Generation (RAG) applications.
• Build RAG pipelines using enterprise documents and structured/unstructured data sources.
• Design document ingestion, parsing, chunking, embedding, indexing, retrieval, and generation workflows.
• Implement semantic search and context retrieval mechanisms.
• Improve retrieval relevance, context quality, and LLM response accuracy.
• Develop production-ready RAG architectures that can scale with enterprise workloads.
• Develop AI applications and backend services using Python.
• Build scalable APIs using FastAPI.
• Develop RESTful APIs for AI applications and LLM-powered services.
• Integrate AI services with databases, enterprise applications, and external APIs.
• Implement authentication, validation, error handling, logging, and API security.
• Develop AI workflows using LangChain and LangGraph.
• Build structured LLM workflows and agentic/RAG pipelines.
• Implement prompt templates, chains, retrievers, tools, and workflow orchestration.
• Design stateful workflows using LangGraph where required.
• Integrate multiple AI components into production-ready applications.
• Integrate LLM platforms such as:
• OpenAI
• Google Gemini
• Anthropic Claude
• Develop LLM-powered applications based on business requirements.
• Implement prompt engineering and context-management strategies.
• Optimize LLM responses for relevance, accuracy, consistency, and latency.
• Handle LLM API integration, error handling, rate limits, and production considerations.
• Work with vector databases such as:
• PGVector
• Pinecone
• FAISS
• Implement embedding generation and vector indexing.
• Design semantic and similarity-search solutions.

Qualifications

• Relevant Hands-on Experience: 1–3 Years in RAG / AI Development
• Location: PAN India
• Employment Type: Contract / Project-Based
• Openings: 3 Positions
• Note: Only immediate Joiner will be considered, 30/60/90 days notice period will not consider only apply if you are immediate joiner
• Job Summary
• We are looking for skilled RAG Engineers / AI Developers with strong hands-on experience in Retrieval-Augmented Generation (RAG), Python, FastAPI, LangChain, LangGraph, LLM integration, vector databases, and AI application development.
• The ideal candidate should have practical experience building production-ready RAG applications, integrating enterprise data sources with LLMs, developing document-processing pipelines, and implementing semantic search and vector-based retrieval solutions.
• The candidate will work closely with AI/ML, backend, data, and engineering teams to design and deliver scalable Generative AI and RAG solutions for enterprise use cases.
• Retrievers, chains, tools, and workflow orchestration
• PGVector
• Pinecone
• FAISS
• OpenAI
• Gemini
• Claude
• PostgreSQL
• SQL
• Git
• Docker
• 4–7 years of total professional experience.
• 1–3 years of relevant hands-on RAG / AI development experience.
• Strong Python development experience.
• Hands-on experience building RAG applications.
• Hands-on experience with at least one of:
• Hands-on experience integrating one or more:
• Good to Have
• Agentic AI
• Multi-agent systems
• Hybrid Search