A production AI/ML systems engineer with 10+ years taking models from the research notebook to live customer traffic. At Best Buy I built a config-driven pipeline that fine-tunes open-source language models (LoRA) and serves them in-house — replacing paid external AI APIs to save $300K/year — powering real-time fraud detection and personalization at 40,000+ transactions/sec. Earlier, at Wipro, I built GPU-accelerated cybersecurity ML on an NVIDIA DGX supercomputer (8×A100): models that learn each cloud account's normal behaviour from billions of rows of security logs to surface intrusions; a phishing-email detector running at 2× the previous speed; and automated triage of the customer "abuse" mailbox — where shoppers forward suspected scams and fraud — handling 2× the case volume and routing each threat to the right security team. Before that, an Azure ML system predicting product quality on arrival saved $2.5M/year, and Google OR-Tools route optimization streamlined last-mile delivery. Across 10 years and six companies I've earned the top performance rating at every one — the specialist for the gap between works in a notebook and serves real traffic.
Config-driven LoRA adapter training for open-source causal LLMs with YAML-driven task addition — no Python required for new use cases. Unsloth backend for ~2× faster training and 60-80% VRAM reduction via QLoRA; HF Accelerate for single/multi-GPU. vLLM serving on GKE with hot-swap adapter support, HPA autoscaling, Istio routing, and GCS-backed model store.
Unsupervised autoencoder models trained per-entity (account / user / service / machine) to profile normal AWS activity patterns and surface anomalous deviations in real time. Training and inference pipelines engineered with NVIDIA Morpheus on DGX (8×A100). 20+ days of logs across multiple AWS accounts totaling billion-row datasets per run; model management via MLflow; alerts served via Grafana.
Vehicle Routing Problem (VRP) formulation using Google OR-Tools for a major logistics operator — optimizing route planning, departure scheduling, and product load distribution across a delivery fleet. Constraint modelling accounted for time windows, vehicle capacity, and geographic clustering.
End-to-end Azure ML pipeline predicting product quality at warehouse arrival for a Norwegian retail group — enabling proactive inspection scheduling and reducing downstream quality failures. Fully operationalized with automated triggers and pipeline orchestration.