Job
Description
Role Overview: As the Agentic AI Lead, you will play a crucial role in spearheading the research, development, and implementation of semi-autonomous AI agents to tackle intricate enterprise challenges. Your responsibilities will include leveraging LangGraph to construct multi-agent AI systems with enhanced autonomy, adaptability, and decision-making capabilities. You are expected to possess profound knowledge in LLM orchestration, knowledge graphs, reinforcement learning, and practical AI applications, positioning yourself as a pioneer in the domain of next-gen AI automation. Key Responsibilities: - Architecting & Scaling Agentic AI Solutions - Design and build multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. - Develop memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across diverse domains. - Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. - Hands-On Development & Optimization - Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. - Implement knowledge graphs, vector databases, and retrieval-augmented generation techniques for enhanced agent reasoning. - Apply reinforcement learning methodologies to fine-tune AI agents for improved decision-making. - Driving AI Innovation & Research - Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. - Stay abreast of advancements in multi-agent systems, AI planning, and goal-directed behavior, and apply best practices to enterprise AI solutions. - Prototype and experiment with self-learning AI agents that adapt autonomously based on real-time feedback loops. - 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 showcase tangible business impact and scale successful prototypes into production. - Mentorship & Capability Building - Lead and mentor a team of AI Engineers and Data Scientists, nurturing 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. Qualifications Required: - Hands-on experience with Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, and Statistical analysis. - Proficiency in Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting techniques, Classification algorithms, ML Frameworks, and Distance metrics. - Strong background in AI research, development, and deployment, particularly in the areas of LangGraph, LLM orchestration, and reinforcement learning. Role Overview: As the Agentic AI Lead, you will play a crucial role in spearheading the research, development, and implementation of semi-autonomous AI agents to tackle intricate enterprise challenges. Your responsibilities will include leveraging LangGraph to construct multi-agent AI systems with enhanced autonomy, adaptability, and decision-making capabilities. You are expected to possess profound knowledge in LLM orchestration, knowledge graphs, reinforcement learning, and practical AI applications, positioning yourself as a pioneer in the domain of next-gen AI automation. Key Responsibilities: - Architecting & Scaling Agentic AI Solutions - Design and build multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. - Develop memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across diverse domains. - Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. - Hands-On Development & Optimization - Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. - Implement knowledge graphs, vector databases, and retrieval-augmented generation techniques for enhanced agent reasoning. - Apply reinforcement learning methodologies to fine-tune AI agents for improved decision-making. - Driving AI Innovation & Research - Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. - Stay abreast of advancements in multi-agent systems, AI planning, and goal-directed behavior, and apply best practices to enterprise AI solutions. - Prototype and experiment with self-learning AI agents that adapt autonomously based on real-time feedback loops. - AI Strategy & Business Impact - Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. - Lead Agentic AI proof-of-concept (P
