Experience & Skills

Technical Skills & AI Stack

LLM & Generative AI Systems

GraphRAG / RAG Agentic Workflows vLLM & Ollama MCP (Model Context Protocol) Semantic Kernel & AutoGen CrewAI Prompt Engineering LLM Fine-Tuning LoRA / QLoRA RLHF & DPO Model Unlearning Vector DBs (FAISS, Chroma, Pinecone)

Deep Learning & Computational Audition

PyTorch HuggingFace Sound Event Detection Weak / Imprecise Label Learning Multimodal Video & Audio Acoustic Signal Processing JAX & TensorFlow CUDA & DeepSpeed ONNX & TensorRT Audio-Visual Transformers

Languages & Core Technologies

Python (Expert) Bash / Shell Scripting SQL C / C++ SystemVerilog / Verilog Git & GitHub Actions MATLAB HTML / CSS / JavaScript

Cloud Platforms & MLOps

Microsoft Azure (Azure AI) AWS (Bedrock, SageMaker) Google Cloud (Vertex AI) Docker & Containers Kubernetes MLflow & W&B Ray Triton Inference Server

Professional & Industry Leadership

LLM Architecture Associate Director

Accenture — Center for Advanced AI

Present

Led architecture and development of Fortune Analytics — a generative AI business-intelligence platform built in partnership with Fortune Media analyzing 20+ years of Fortune 500 data.
Recognized with the prestigious iF Design Award 2025 (User Interface) and filed European patent EP4660825A1 (LLM data visualization architecture).
Spearheaded enterprise reasoning models and agentic architectures on Microsoft Foundry, presenting Accenture's reasoning-model framework at Microsoft Ignite 2025.
Lead engineering teams delivering state-of-the-art agentic workflows, GraphRAG systems, and enterprise LLM integrations.

Deep Learning Scientist

ReviveMed

2017

Developed AI-driven drug discovery models for metabolic diseases.
Leveraged tens of thousands of metabolomic data points to discover novel biological pathways and impactful therapeutics.

Verification Engineer & Project Lead

ARM — Systems & Software Group

2015 – 2017

Designed and deployed in-house verification platforms to simultaneously verify 52 compute subsystems delivered to silicon partners globally.
Served as Project Lead for the first successful chip-to-chip subsystem verification on ARM architecture.
Consecutively rated amongst the top 5% engineers globally within ARM for two years.
Recipient of the ARM Bravo Award in 2016 & 2017 for uncovering critical RTL bugs and accelerating delivery throughput by 2x.

Component Design Engineer (Intern)

ARM — Systems & Software Group

May – July 2014

Designed Low Power Interface protocols for AMBA 4 and ACE specifications across multi-cluster CPU arrangements.

Academic Research & Education

Ph.D. in Language Technologies

Carnegie Mellon University — School of Computer Science

2017 – 2024

Thesis: "Computational Audition with Imprecise Labels" (Advised by Prof. Bhiksha Raj & Prof. Rita Singh, CMU MLSP Group).

Researched weakly-supervised sound event detection, learning from label noise, and multimodal video analysis.
Published 60+ peer-reviewed papers across top venues including NeurIPS, ICLR, ICASSP, IJCAI, ACM ICMI, and IEEE/ACM TASLP.
Recipient of CMU LTI Fellowship and Conference Travel Grants.

M.S. in Language Technologies