The metamorphosis
Five phases. One compounding arc.
Data Scientist and applied ML researcher with 15 years building production AI systems at the intersection of large language models, graph analytics, and enterprise compliance. Currently architecting multi-agent LLM orchestration and knowledge graph infrastructure for regulatory workflows in financial services — and independently researching in-context learning theory and model compression. I write about the gap between ML research and production reality: what the papers don't tell you, what breaks at scale, and what's worth building from scratch.
Below: the arc that got me here — not a career history, but a sequence of transformations where each identity was prerequisite to the next.
01
2009 — 2014
The enterprise apprentice
Five years writing code against business specs at Cognizant. Installed the domain intuition that every later phase would draw on — you can't architect regulated systems without having lived inside their logic first.
Stopped asking "what does the spec say" — started asking "what does the data say."
02
2014 — 2016
The model builder
First pure ML role at EXL. Built, validated, and retrained predictive models across the full lifecycle. Learned early: a model without a retraining pipeline is a ticking clock.
Models work. But who's watching them drift?
03
2016 — 2019
The signal hunter
Applied ML to revenue problems — churn, refinance targeting, order fallout. Mined unstructured product data at scale to extract sentiment, causal drivers, and latent themes. First time owning the full arc from raw data to business recommendation.
Moved to the US. The question became: how do you govern this at enterprise scale?
04
2019 — 2023
The systems architect
The model-builder became a systems-thinker. Real-time fraud detection via Kafka. Credit scoring for a major bank. COVID-era risk adjustment. Then the graph era — ontologies, RDF knowledge bases, data fabric architecture.
LLMs arrived. The graph work and NLP instincts converged.
05
2023 — now
Current
The orchestrator-researcher
Dual track. By day: multi-agent LLM orchestration on AWS Bedrock with Neo4j for regulatory compliance. By night: formalizing in-context learning theory, deconstructing GPTQ from scratch, publishing the internals.