AI Lead

Agentic AI Lead

Location: Basking Ridge, NJ/Dallas, TX and Tampa, FL Hybrid

Primary - LangGraph, ReAct, LangChain, LlamaIndex, Python

Secondary - GCP, Google Spanner/Neo4j, CrewAI, AutoGen, OpenAI

The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation.

Key Responsibilities:

1. Architecting & Scaling Agentic AI Solutions
   • Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving.
   • Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains.
   • Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications.
2. Hands-On Development & Optimization
   • Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability.
   • Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning.
   • Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making.
3. Driving AI Innovation & Research
   • Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents.
   • Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions.
   • Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops.
4. AI Strategy & Business Impact
   • Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings.