Description

You will build production-ready RAG architectures and AI-driven backend services in Python.

This role is on-site.

Responsibilities

• Develop RAG architectures including embeddings, similarity search, and retrieval optimization.
• Build document ingestion pipelines for loading, parsing, chunking, embedding, and storage with strong metadata handling.
• Implement streaming APIs using Server-Sent Events (SSE) for real-time AI responses.
• Integrate Model Context Protocol (MCP) or similar agent frameworks for context-aware LLM behavior.
• Ensure reliable indexing, source attribution, and traceability for high-quality AI outputs.

Required Skills

• 5+ years of strong experience in Python application development and backend engineering.
• Expertise in Retrieval Augmented Generation (RAG) architectures and workflows.
• Hands-on experience with vector databases, embeddings, and retrieval optimization.
• Experience building document parsing and extraction workflows for structured and unstructured data.
• Strong understanding of document ingestion pipelines and metadata-driven indexing.
• Knowledge of LLM integration patterns and context-aware AI application development.
• Experience implementing MCP or similar agent/context integration frameworks.
• Expertise in building Streaming APIs using SSE for real-time responses.
• Strong understanding of scalable backend architecture and asynchronous processing.

Preferred Skills

• Experience with LangChain and LangGraph frameworks.
• Exposure to chat-based and agent-based UI integrations.

Key Skills

Education

Any Graduate