Agentic AI Lead

Agentic AI Lead

Dallas, Texas, OR Tampa FL (Onsite)

1 Year

Job requirements

1. 1. 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

1. 1. Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving.
   2. Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains.
   3. Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications.

2. Hands-On Development & Optimization

1. 1. Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability.
   2. Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning.
   3. Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making.

3. Driving AI Innovation & Research

1. 1. Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents.
   2. Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions.
   3. Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops.

4. AI Strategy & Business Impact

1. 1. Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings.
   2. Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production.

5. Mentorship & Capability Building

1. 1. Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures.
   2. Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents.

Required Skills & Experience

Strong hands-on experience with LangGraph and multi-agent AI development

Proficiency in LLM orchestration (LangChain, LlamaIndex, OpenAI Function Calling)

Expertise in reinforcement learning (RLHF, RLAIF) and self-improving AI agents

Knowledge graph construction & RAG implementation for enhanced agent reasoning

Experience deploying AI agents in production (Google Cloud Platform)