Description

Architect and scale Agentic AI solutions by designing multi-agent systems using LangGraph for workflow automation and autonomous problem-solving.

This role is hybrid.

Responsibilities

• Design and implement scalable architectures for LLM-powered agents integrated with enterprise applications.
• Develop and optimize agent orchestration workflows using LangGraph, incorporating knowledge graphs and RAG techniques.
• Lead research into Agentic AI, multi-agent systems, and self-improving AI agents.
• Translate Agentic AI capabilities into enterprise solutions to drive automation and efficiency.
• Mentor AI Engineers and Data Scientists on LangGraph and multi-agent architectures.

Required Skills

• 5+ years of professional experience in AI development.
• Proficiency in Python.
• Experience with PySpark and TensorFlow or PyTorch.
• Familiarity with machine learning libraries such as Sci-kit Learn.
• Knowledge of agent orchestration frameworks, specifically LangGraph.
• Experience implementing Retrieval-Augmented Generation (RAG) patterns.
• Exposure to reinforcement learning methodologies (RLHF/RLAIF).
• Working knowledge of vector databases (Pinecone, FAISS).
• Familiarity with PySpark or SAS/SPSS.

Key Skills

Education

Any Gradute