Data Scientist

• 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.
• 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.
• 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.
• 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.

Data Scientist

Senior Principal Data Scientist, Real World Evidence

Director, R&D Data Science – AI Knowledge & Literacy Lead

Director, R&D Data Science – AI Tools Expert

Director, R&D Data Science – AI Knowledge & Literacy Lead

Senior Principal, R&D Data Science, Digital Health, Real-World Evidence Statistics