Data Scientist - R01560291

**Senior Lead AI/ML Engineer** **Primary Skills** * Generative Design Concepts Job requirements * JD is below: 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. * Lead Agentic AI proof\-of\-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5\. Mentorship \& Capability Building * Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi\-agent architectures. * Establish best practices for model evaluation, responsible AI, and real\-world deployment of autonomous AI agents.