About the Role

We are looking for an AI Engineer to help design and develop advanced agentic AI systems using

frameworks such as LLM, RAG, LangChain, LangGraph, and CrewAI. The role involves working on complex,

open-ended problems that require creativity, sound judgment, and thoughtful execution.

The ideal candidate for us is the one who brings a strong sense of ownership, curiosity, and technical

depth someone who enjoys turning broad goals into well-functioning, impactful systems.

Responsibilities

Design and develop AI agents using LangChain, LangGraph, or CrewAI.

Implement Retrieval-Augmented Generation (RAG) architectures for context-driven conversational systems.

Integrate knowledge graphs and memory modules to improve reasoning and contextual continuity.

Build and manage long-running or event-driven agent workflows for automation tasks.

Collaborate with cross-functional teams to integrate agents into production systems.

Evaluate and fine-tune open-source LLMs for domain-specific tasks (preferred). Requirements

Strong programming skills in Python.

Experience with LangChain, LangGraph, or CrewAI.

Understanding of vector databases (e.g., FAISS, Chroma, Qdrant, Weaviate).

Familiarity with RAG architecture, prompt engineering, and LLM APIs (OpenAI, Claude, etc.).

Exposure to knowledge graphs and persistent agent memory (bonus).

Experience with fine-tuning or running open-source LLMs like LLaMA, Mistral, or Falcon

(bonus).

Good understanding of containerization (Docker) and deployment practices.

Nice to Have

Practical exposure to LangGraph orchestration or CrewAI multi-agent systems.

Knowledge of MCP (Model Context Protocol) or tool-using agents.

Hands-on experience with model optimization and resource-efficient inference.

What We Offer

Opportunity to work on cutting-edge agentic AI systems.

Collaborative environment with research-driven projects.

Exposure to LLM-based automation and enterprise-grade AI applications

AI Engineer