Key Responsibilities:

Agentic AI System Design & Development

Design and build multi-agent systems, autonomous agents, and tool-using AI workflows.
Develop orchestration frameworks for agent collaboration using LLM-based architectures.
Implement human-in-the-loop and decision-support systems for enterprise applications.
Optimize agent performance for reliability, scalability, and cost efficiency.

LLM Engineering & Productionization

Fine-tune and prompt-engineer Large Language Models (open-source and proprietary).
Build and deploy production-grade LLM applications including copilots, chat systems, and intelligent automation tools.
Design and implement Retrieval-Augmented Generation (RAG) pipelines.
Evaluate and optimize LLM performance across accuracy, latency, and cost metrics.

GenAI Frameworks & Ecosystem

Work with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen.
Integrate LLMs with enterprise data sources and APIs for real-world use cases.
Develop reusable GenAI components and scalable architecture patterns.
Stay current with emerging GenAI tools and research advancements.

Data Science & Machine Learning

Translate business problems into well-defined ML and AI solutions.
Perform advanced data wrangling, feature engineering, and exploratory data analysis.
Apply statistical modeling, deep learning, NLP, and machine learning techniques.
Drive experimentation, hypothesis testing, and model evaluation.

MLOps / LLMOps & Production Systems

Design and implement CI/CD pipelines for ML and LLM systems.
Ensure model monitoring, versioning, governance, and performance tracking.
Optimize infrastructure for scalable deployment on cloud platforms (AWS, Azure, or GCP).
Implement responsible AI practices including explainability, fairness, and risk management.

Insight Generation & Stakeholder Communication

Leadership & Mentorship

Innovation & Problem Solving

Principal Data Scientist - R01559946