Senior Data Science Lead - R01551331

About the Job

Senior Data Science Lead: Focus on developing and deploying semi-autonomous AI agents. This role involves hands-on experience with LangGraph, building multi-agent AI systems, and expertise in LLM orchestration, knowledge graphs, and reinforcement learning. Key responsibilities include architecting and scaling Agentic AI solutions, hands-on development and optimization, driving AI innovation and research, AI strategy and business impact, and mentorship and capability building. The ideal candidate will have expertise in areas like Hypothesis Testing, Regression, Statistical Analysis, and ML frameworks.

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.