work across the stack
- machine learning
- NLP, predictive modeling, ranking, monitoring, drift detection, and governance.
- generative AI
- Retrieval, synthesis, evaluation, guardrails, and observability for systems built on foundation models.
- agentic systems
- Tool use, planning, routing, recovery, human handoffs, and the conditions under which a system should earn more autonomy.
building
agent-economics-lab
One current project focuses on the decision layer around agents. Once a system can complete a task, what evidence should determine whether it scales, gets assistance, or stops?
agent-economics-lab is a small, open-source, dependency-free
decision engine that maps traces and outcomes to four actions:
INCOMPLETE, SCALE, ASSIST, or
STOP. The benchmark is intentionally mean to the evaluator:
remove evidence and a naive cost optimizer confidently emits dozens of false
SCALE decisions. The fail-safe policy emits zero.
writing
external receipts
A few things the internet can verify without trusting this page.
- Institute of Analytics Fellow
- The Webby Awards Judging academy
- IEEE Women in Engineering Industry expert
- Semantic grouping with network graphs US Patent 11,748,453
- Navigating the Complexities of Generative AIs IBM Research · ADSA 2024
- Building Retrieval Augmented Generation UCLA Extension · Instructor
speaking
I occasionally speak about production machine learning, generative AI, agentic systems, evaluation, and responsible deployment.
Selected venues: PyData · Responsible AI Institute · IBM's Mixture of Experts podcast · IBM TechXchange (speaker and lab instructor)
For talks: gajjar.vyoma@gmail.com
teaching
Since 2021, I have taught machine learning, deep learning, production AI, RAG, generative AI evaluation, and responsible deployment through MIT Sloan (Great Learning), UCLA Extension, UT Dallas, and Maven.
about
I have spent the last decade building and operating AI systems, from NLP and predictive models to RAG pipelines and tool-using agents.
The through line is production: how to make models measurable, observable, steerable, and useful once they leave the notebook.
I am interested in systems that learn from feedback without hiding failure.
San Francisco Bay Area.
Email LinkedIn GitHub