Market Risk Analytics
Quantitative finance toolkit with Black-Scholes option pricing, Monte Carlo Value-at-Risk, and historical stress testing — wrapped in an interactive dashboard.
- Python
- NumPy
- Monte Carlo
- Streamlit
Hi, my name is
Machine Learning Engineer & Full-Stack Developer crafting intelligent, production-grade software — from quantitative risk engines and NLP systems to end-to-end web applications that people actually use.
$ whoami
ml-engineer & full-stack developer
$ ls ~/skills
python pytorch fastapi react
sql docker kubernetes rag/llms
$ cat status.txt
▸ open to opportunities & collabs
$
I'm an engineer who enjoys the whole journey — from a rough idea on a whiteboard to a deployed system serving real users. My work sits at the intersection of machine learning and full-stack engineering: I've built quantitative risk toolkits, NLP-driven document analysis platforms, recommendation systems, and the web applications that put them in people's hands.
I've trained through Stanford's Deep Learning Specialization (Andrew Ng), Angela Yu's Full-Stack bootcamp, and professional DevOps & Data Science certifications — and I sharpen all of it in hackathons, where shipping fast under pressure is the whole game.
Quantitative finance toolkit with Black-Scholes option pricing, Monte Carlo Value-at-Risk, and historical stress testing — wrapped in an interactive dashboard.
AI-powered analysis of Detailed Project Reports for Indian government projects — NLP pipelines for quality scoring and risk prediction on a PostgreSQL backend.
Models day-to-day loan operations: an accrual & repricing engine, invoicing with reconciliation, a document/risk dashboard, and a full audit trail.
Procure-to-pay simulator of the SAP MM flow — purchase requisition to PO to goods receipt to invoice, with three-way match validation.
Retrieval-augmented generation built from first principles — document chunking, embeddings, vector search, and grounded LLM answers with no framework magic.
Real-time visualization of model training — watch loss curves, metrics, and learning dynamics stream live into a Streamlit dashboard.
Stanford / Andrew Ng · Coursera
Neural networks, CNNs, RNNs, optimization, and structuring real ML projects.
Angela Yu · Udemy
Complete bootcamp: HTML/CSS, JavaScript, Node.js, React, REST APIs, and databases.
Simplilearn
Git, Jenkins CI/CD pipelines, Kubernetes orchestration, and Selenium automation.
Simplilearn
Statistical modeling in R with ARIMA and time-series analysis for research work.
I'm open to ML engineering and full-stack roles, freelance work, and interesting collaborations. If you have a problem that needs intelligent software — or just want to talk shop — my inbox is always open.