Software engineer currently working on continuous AI-agent evaluation at Eli Lilly and Company. My background spans real-time voice AI, applied ML research at UNLV, and quantitative risk modelling - and I'm now working toward a transition into quantitative finance through derivatives pricing, Monte Carlo simulation, and machine learning for risk.
About
I enjoy exploring the intersection of engineering and quantitative research, building systems that are both fast in production and rigorous in their modelling. When I'm not coding, you'll find me reading about systems design and financial markets, or embarking on outdoor adventures.
Work
Eli Lilly and Company
CurrentTech Intern
Jul 2026 - Present
Building continuous evaluation pipelines that measure AI-agent quality, reliability, and safety across releases, with real-time observability into agent behavior.
Geosentry.AI
AI Intern
May 2026 - Jun 2026
Built core personalized voice companion architecture, custom connectors, and memory graphs.
reAlpha
SWE Intern
Sep 2025 - Mar 2026
Built infrastructure for real-time voice AI with WebRTC, reducing end-to-end voice response latency by ~900ms across production workloads.
University of Nevada, Las Vegas
Research Intern
Jun 2025 - Aug 2025
Developed ML systems achieving 0.89 micro F1 for protein function prediction and a 0.853 ROC-AUC financial risk-scoring model.
Vedic Sadhana Foundation
Software Intern
Oct 2024 - Dec 2024
Built production-grade scraping and NLP pipelines processing 50,000+ entries with automated data cleaning and API endpoints.
Skills
Python
Java
SQL
C
Bash
PyTorch
HuggingFace
NumPy
FastAPI
PostgreSQL
Redis
AWS
Azure
GCP
Docker
Git
GitHubEducation
BNM Institute of Technology
Dec 2022 - Jul 2026B.E. in Computer Science
Final-year coursework focused on machine learning, distributed systems, and software engineering.
University of Nevada, Las Vegas
Jun 2025 - Aug 2025Visiting Student - Research Intern
Conducted research in machine learning and quantitative risk modelling, including protein function prediction and financial transaction risk scoring.
Projects
Options Pricing & Monte Carlo Simulation Framework
Quantitative derivatives-pricing framework using Monte Carlo simulation for European and American options - Geometric Brownian Motion price paths, Black-Scholes validation, and Longstaff-Schwartz Least-Squares Monte Carlo for early-exercise decisions.
Quantitative Transaction Risk Modeling
Explainable machine-learning framework for quantitative transaction risk scoring using temporal, behavioral, and network features - an XGBoost classifier achieving a 0.853 ROC-AUC with calibrated probabilities, out-of-time validation, and SHAP-based feature attribution.
Publications
SafeStreets: Deep Learning-Based Real-Time Traffic Violation Detection System
Proposed an AI-driven edge surveillance architecture that automates traffic violation detection (including helmet rule compliance and automatic license plate recognition). Deployed directly onto latency-constrained edge hardware with highly optimized neural pipelines.
Neural Art Style Transfer
Explored real-time style harmonization utilizing deep convolutional networks (VGG-19) to align artistic styles with camera input. Optimizes feed-forward neural layers to maintain high temporal consistency and prevent frame flickering in live video feeds.
Let's work together.
Open to new opportunities, collaborations, and interesting conversations - my inbox is open.
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