Building the platforms & infrastructure AI runs on
I work in the unglamorous middle layer of AI — turning research and prototypes into systems teams can depend on. 0→1 is my default: ML platforms, agentic and GenAI infrastructure, and the whole path to production.
The part I love is the bridge — making research-stage ideas hold up and scale, including the bits people skip: governance, cost, adoption.
Outside that: a bike, a race, travel, or a new language (not the programming kind).
Building ML/AI platforms and agentic + GenAI infrastructure — from RL-driven self-optimizing 5G networks to an enterprise Data & AI platform.
Audience-modeling R&D for Advertising Cloud — end-to-end ML for ad targeting at scale.
Data science for T-Mobile — NLP call-driver classification and BI analytics pipelines.
Master's in Data Science & Business Intelligence. TA for ML & econometrics (IMT 574).
R&D in image processing and augmented / mixed-reality applications.
A RAG assistant that lets teams query their own private documentation in plain language and get answers grounded in real sources — collapsing a 30-minute hunt across thousands of internal docs into under a minute. Covers the architecture: embeddings, vector search, LLM reasoning, and an auto-scaling ingestion pipeline on AWS.
— A three-part series on building a self-perfecting 5G network —
The data layer underneath: an open-source, cloud-agnostic observability stack (Prometheus, Thanos, Loki) that collects logs and metrics from a Kubernetes cluster and feeds them to the RL agent in near real time.
How a reinforcement-learning agent can act as the brain of a 5G network that tunes itself — a closed-loop system where the agent observes a simulated 5G core running as a Kubernetes app and adjusts resources to balance service quality against cost.
A modular, open-source ML framework spanning the full lifecycle — ingestion, data prep, training, deployment — built so teams can swap compute backends without re-plumbing. Applied to anomaly detection on Kubernetes performance logs.