Experience

From neuroscience research to enterprise AI engineering.

2024 – Present

Agentic AI Engineer

IBM

Building enterprise AI systems, multi-agent architectures, and retrieval pipelines — from prototype to production.

  • Architected multi-agent orchestration systems with orchestrator and specialist sub-agents
  • Built RAG systems with chunking, embeddings, vector retrieval, and grounded generation
  • Developed Python tool-calling frameworks for enterprise APIs and workflows
  • Built evaluation and testing pipelines for agent quality and workflow success
  • Supported production deployment of enterprise AI systems and published agents to IBM Agent Catalog
  • watsonx
  • Orchestrate ADK
  • Python
  • RAG
  • embeddings
  • vector databases
  • LoRA
  • LLM evaluation
  • tool calling
  • multi-agent systems
2022 – 2024

AI Engineer / Data Scientist

IBM

Building machine learning, NLP, and early generative AI systems for enterprise clients.

  • Built enterprise data science and NLP solutions
  • Developed generative AI proof-of-concepts and pilot systems
  • Led customer-facing architecture and design sessions
  • Worked across machine learning, LLMs, and applied enterprise AI delivery
  • Python
  • NLP
  • LLMs
  • LangChain
  • data science
  • enterprise architecture
2020 – 2022

Applied Research Scientist

General Dynamics (EPA)

Research and engineering work focused on OCR, NLP, and document intelligence in high-performance computing environments.

  • Deployed OCR systems across document databases
  • Trained and deployed NLP and document classification models
  • Used transfer learning with RoBERTa and Hugging Face tooling
  • Built similarity workflows using transformer embeddings and cosine similarity
  • PyTorch
  • Hugging Face
  • OCR
  • Tesseract
  • RoBERTa
  • NLP
  • cosine similarity
  • HPC
2016 – 2019

Senior Data Analyst

Johns Hopkins Hospital

Analytics, reporting systems, and healthcare data workflows.

  • Built Python and pandas workflows for difficult-to-access datasets
  • Performed exploratory factor analysis on client survey data
  • Supported executive and stakeholder reporting needs
  • Improved stability, security, and performance of an internal case management system
  • Python
  • pandas
  • analytics
  • factor analysis
  • reporting systems
2012 – 2016

Research

Johns Hopkins, NIDA, University of Minnesota

Neuroscience, psychology, and clinical research grounded in data, signals, and behavior.

  • Worked on neuroscience and psychology research projects
  • Analyzed electrophysiological and behavioral data
  • Helped launch clinical research workflows and research operations
  • Contributed to published research
  • statistics
  • PCA
  • time series analysis
  • experimental design
  • research methods
  • electrophysiology