AI Engineer_5+ years

We are looking for a hands-on AI Engineer to help build our foundational AI infrastructure from the ground up. The ideal candidate will have strong experience in Python, LangChain, Agentic AI, RAG, vector databases, and LLM applications.

You will design, develop, evaluate, and deploy intelligent applications with a strong focus on Agentic AI and Retrieval-Augmented Generation (RAG) systems.

Key Responsibilities

• Design and develop production-ready RAG architectures and Agentic AI workflows.


• Build robust data ingestion and processing pipelines for unstructured and semi-structured data.


• Work with web crawlers, OCR technologies, document parsing, and Docling to extract and structure information.


• Develop LLM-powered applications using Python and LangChain.


• Build and integrate AI agent tools using Model Context Protocol (MCP).


• Implement vector databases, embeddings, semantic search, and retrieval pipelines.


• Evaluate and benchmark LLM/RAG applications using Ragas and other evaluation techniques.


• Perform prompt engineering, response evaluation, and optimization to improve accuracy and relevance.


• Design scalable and maintainable AI solutions suitable for production environments.


• Collaborate with engineering teams to define AI architecture, development standards, and best practices.


• Contribute to technical decisions in a greenfield AI environment and help establish the organization's AI technology foundation.

Required Skills

• Strong proficiency in Python.


• Hands-on experience with LangChain.


• Strong experience building Agentic AI / AI Agent systems.


• Deep understanding of RAG architecture and LLM application development.


• Experience with Vector Databases, embeddings, semantic search, and retrieval techniques.


• Experience with Ragas or LLM evaluation frameworks.


• Strong knowledge of Prompt Engineering.


• Hands-on experience with OCR, document parsing, Docling, and web crawling.


• Understanding of MCP (Model Context Protocol) and agent tool integration.


• Experience working with LLMs, APIs, and AI application development.

Nice to Have

• Experience deploying ML/AI models into production.


• Experience with AWS, GCP, or Azure.


• Knowledge of Docker, Kubernetes, CI/CD, and MLOps.


• Experience with additional LLM frameworks such as LlamaIndex, LangGraph, or Hugging Face.


• Experience working with databases such as PostgreSQL, MongoDB, or vector databases.

Key Skills

Python | LangChain | Agentic AI | RAG | Vector Database | LLM | Ragas | MCP | Prompt Engineering | OCR | Docling | Web Crawling