Software Engineering · AI Systems · Infrastructure
Hey, I'm Paramveer.
Software engineer building AI systems, backend infrastructure, and production products. Columbia M.S. Artificial Intelligence ’27 · USF Computer Science ’26.
Five projects that show how I approach AI systems, backend workflows, product engineering, and software used beyond the classroom.
01 — AI INFRA · DEVELOPER TOOL
Polaris
AI-powered GitHub App that reviews Infrastructure-as-Code pull requests, maps findings to security controls, and generates verified fixes before merge.
Infrastructure changes often reach review without fast, actionable security feedback across Terraform, Dockerfiles, Kubernetes YAML, and GitHub Actions.
What I built
Built the GitHub App workflow, FastAPI analysis service, repository-scoped data model, control mapping, and verified patch flow used inside pull requests.
Architecture / approach
01GitHub pull request webhook→
02FastAPI analysis service→
03Dual-agent review and fix verification→
04PostgreSQL findings store→
05PR comments and developer-approved fixes
Engineering challenge
Keeping repositories isolated while turning model output into precise, reviewable findings and patches across several configuration formats.
Result
The working app posts mapped security findings in pull requests and verifies generated patches before presenting them to developers.
02 — DEVELOPER TOOL · OPEN SOURCE
PR Nutrition
v0.4.0 · CLI + GitHub Action
Local-first, deterministic CLI and GitHub Action that labels pull-request review risk, filters low-value files, and points reviewers to the changes that deserve attention first.
Reviewers can lose time sorting generated files, lockfiles, and broad pull-request diffs before finding the changes that carry the most review risk.
What I built
Built the deterministic analyzer, CLI commands, configuration flow, review-focus reports, and read-only GitHub Action used to prioritize a pull request without sending source code to an external service.
Architecture / approach
01Local Git metadata→
02Path and change classifier→
03Risk and readiness model→
04Markdown and JSON reports→
05CLI and GitHub Action
Engineering challenge
Producing useful, repeatable review guidance while staying local-first and avoiding source-code uploads, external APIs, or model calls.
Result
The current v0.4.0 release is distributed through npm and a GitHub Action, with deterministic output designed for both terminal and CI workflows.
03 — ML RESEARCH · AI4ALL
Adversarial Spam Detection
91.7% → 95.7% adversarial detection
Built an adversarial training pipeline pairing a BERT spam classifier with Qwen-generated attacks, improving adversarial detection from 91.7% to 95.7% across three training iterations while maintaining 96.5% accuracy.
A classifier that performs well on familiar messages can still fail when an attacker deliberately rewrites spam to evade its learned patterns.
What I built
Built the iterative training and evaluation pipeline: fine-tuned BERT, generated hard examples with Qwen3-4B and LoRA, retrained, and compared errors after each round.
Architecture / approach
01SMS dataset and BERT baseline→
02Qwen3-4B + LoRA attack generation→
03Adversarial example filtering→
04Classifier retraining→
05Clean and adversarial evaluation across three iterations
Engineering challenge
Improving robustness against generated attacks without sacrificing performance on the original evaluation set.
Result
Adversarial detection improved from 91.7% to 95.7% while clean accuracy remained at 96.5%.
04 — AWARD WINNER · AI PRODUCT
CarbonCTRL
Winner — MLH Best Use of Gemini API · HackaBull 2025
Award-winning carbon management platform that tracks environmental impact, visualizes sustainability data, and uses Gemini to generate personalized reduction strategies.
Organizations need a clearer way to connect emissions inputs with understandable impact data and practical reduction steps.
What I built
Built the product experience and full-stack workflows for emissions calculations, authenticated dashboards, stored user data, and Gemini-generated reduction plans.
Architecture / approach
01React + TypeScript dashboard→
02Node.js and Express API→
03MongoDB persistence→
04Gemini recommendation workflows→
05Python ML services
Engineering challenge
Evolving the original hackathon prototype into a clearer service architecture and migrating persistence from Supabase to MongoDB without losing the core user workflow.
Result
Won the MLH Best Use of Gemini API award at HackaBull 2025, which had 63 submitted projects.
05 — CLIENT WORK · PRODUCTION
Coefficient Software Systems
Rebuilt and shipped Coefficient Software Systems’ production corporate website, organizing products, services, industry verticals, careers, company information, and contact flows into one consistent experience.
Rebuilt the original lawyer recommendation app as a multi-agent legal intake workspace for structured summaries, triage signals, evidence checklists, and attorney matching.
Built scraping and validation tooling to cross-check spreadsheet metadata against USF Digital Commons assets, reducing manual review time by approximately 70%.
Standardized and validated metadata for 1,000+ digital collection records while supporting large-scale collection inventory and data operations.
Reality, Autonomy, and Robot Experience (RARE) Lab, University of South Florida
Feb 2025 – Jul 2025
Contributed to human-robot interaction research on protective indicators designed to mitigate robot abuse, collecting, validating, and analyzing experimental data.
Built an Android research application integrating Gemini and Google Cloud to support structured AI-assisted recipe recommendation study workflows.
Mentored 25+ engineering students through weekly calculus and problem-solving sessions, creating practice exercises and facilitating collaborative learning.
I build software at the intersection of backend engineering and applied AI — from developer tools and ML pipelines to full-stack products and research systems.
01
AI product systems
Model workflows, evaluation, and reliable application boundaries.
02
Backend infrastructure
APIs, persistence, data pipelines, and deployment paths.
03
Product engineering
Interfaces and tools that make technical systems useful.
Background
I graduated from the University of South Florida with a B.S. in Computer Science and minor in Entrepreneurship, and I am pursuing an M.S. in Artificial Intelligence at Columbia University with a focus on AI Infrastructure.
Current focus
I'm particularly interested in APIs, model workflows, infrastructure, data pipelines, evaluation, and the software needed to turn models into reliable products.
Academic path
Education.
Columbia University
M.S. in Artificial Intelligence
2026 – 2027
New York, NY
Focus: AI Infrastructure
University of South Florida
B.S. in Computer Science
2022 – 2026
Tampa, FL
Minor: Entrepreneurship
Beyond the build
Leadership & recognition
A compact view of teams led, programs completed, and work recognized.
Recognition
HealthHacks 2025
Hackathon Judge
Oct 2025
Recognition
HackaBull 2025
Winner — MLH Best Use of Gemini API
April 2025
Leadership
TEDx at USF
Head of Logistics
Jun 2024 – Jan 2025
Leadership
Students of India Association at USF
Vice President
Jan 2024 – May 2024
Leadership
Society of Hispanic Professional Engineers at USF
Database Director
Jan 2024 – May 2024
Program
Goldman Sachs
Virtual Insight Series
May 2024 – Jun 2024
Academic recognition
USF Green & Gold Directors WaiverUniversity of South FloridaUp to $36,000 · $9,000/year
Annette L. Raymund Endowed Scholarship Fund RecipientUniversity of South Florida · Aug 2023
Computer Science and Engineering Fund RecipientUSF College of Engineering · Jul 2025
USF Fund for the College of Engineering RecipientUSF College of Engineering · Jul 2025
Technical toolkit
Skills I use to ship.
Grouped for quick scanning, with project-specific models and tools kept where they have context.
Languages & Frontend
01
Python
TypeScript
JavaScript
Java
C++
React
Next.js
Backend & Data
02
FastAPI
Flask
Node.js
REST APIs
PostgreSQL
MongoDB
SQL
AI / ML
03
PyTorch
TensorFlow
Scikit-learn
Transformers
BERT
LoRA
LLM APIs
Infrastructure & Cloud
04
AWS
Docker
Git
GitHub
Vercel
Render
CI/CD
Contact
Let's build something useful.
I’m interested in software engineering, AI/ML engineering, AI infrastructure, research, and product-focused opportunities.
