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Resume

Education

  1. University of Arizona

    Aug 2025 — May 2027

    Master of Science in Data Science
    GPA: 3.83/4.0

  2. Shiv Nadar University

    Aug 2020 — May 2024

    Bachelor of Technology in Computer Science
    Two times Dean's List Awardee
    Specialization in Data Science and Big Data Analytics

Experience

  1. HCLTech · Software Engineer

    Generative AI Development Team

    July 2024 — July 2025
    • Built custom copilots (Python, TypeScript, LangChain, Hugging Face) integrated with ServiceNow and SharePoint for contextual search across 4,000+ knowledge files, reducing daily service desk tickets by 26% (2,300 to 1,700) and resolution time by 76% (105 min to 25 min), shipped via CI/CD with automated tests.
    • Developed and deployed employee cost models over SharePoint data (Python, pandas, FastAPI, Azure SQL) with predicted cost landing within 7% of actual on average (MAPE), running as a semi-automated weekly workflow on Azure App Service, containerized with Docker and released through Azure DevOps CI/CD.

  2. HCLTech · Software Engineer Intern

    Jan 2024 — July 2024
    • Developed the Sales KPP enterprise application end to end with React, C#, .NET, and MS SQL Server, tracking employee sales targets and achievements against revenue and annual deal closure metrics, replacing manual Excel consolidation for HR, PMO, and L2 leadership.
    • Deployed the application to production and delivered the technical documentation.

  3. Carrier · Web Developer Intern

    May 2023 — July 2023
    • Designed and developed the Account Payable Management System and Ticket Tracker for the sales team using HTML, CSS, JavaScript, jQuery, and jQWidgets.
    • Integrated an FAQ chatbot that cut support queries 38%, from 450 to 280 per week, and added automated tests.

  4. PricewaterhouseCoopers · Technical Intern

    May 2022 — Sep 2022
    • Extracted and parsed project metadata from CNCF repositories with Python and REST APIs, then built a graph database model in Neo4j mapping dependency relationships across cloud native projects.
    • Correlated and visualized the data in Golang backed dashboards running in Docker, surfacing insights for the Advisory team.

Projects

  1. Reflex — Collision Risk DetectionSource Code

    Python, PyTorch, YOLO11, OpenCV, FastAPI, React, TypeScript, PostgreSQL

    • Developed a system that scans traffic video and flags dangerous close calls between cars, cyclists, and pedestrians, using YOLO11, ByteTrack, and OpenCV to detect and track road users at 94.4% vehicle detection F1, validated against the Urban Tracker research benchmark.
    • Built a risk scoring method that flags near misses at 87.6% precision against hand labeled events, optimized the models with TensorRT to cut processing time 3.8x (12 to 46 frames per second) for live feeds, and designed a dashboard that replays flagged events and maps where close calls cluster, revealing a city's riskiest intersections.

  2. RadarMD — Chest X-ray TriageSource Code

    Python, PyTorch, MONAI, timm, Grad-CAM, ONNX, FastAPI, Docker, GCP

    • Developed a deep learning system detecting and localizing 14 thoracic pathologies on chest X-rays, training DenseNet-121 and ConvNeXt with PyTorch Lightning on NIH ChestX-ray14 (112,000+ images, 30,000 patients), reaching 0.84 mean AUROC, a 6 point gain over baseline across 60+ Weights & Biases experiments.
    • Implemented Grad-CAM localization validated against 880 ground truth bounding boxes, with a scikit-learn and torchmetrics test suite prioritizing serious findings over raw accuracy to keep missed diagnoses under 8%.
    • Deployed to production via ONNX export for 4x faster CPU inference (~120 ms/image), serving a Gradio app on Google Cloud Run with GitHub Actions CI/CD, automated tests, and Prometheus health metrics.

  3. Self-Correction for Human Parsing ModelSource Code

    Python, Git

    • Benchmarked the SCHP human parsing model against state-of-the-art methods across Look into Person (LIP), Active Template Regression, and Pascal Part, addressing label noise challenges.
    • Achieved 84.7% mIoU on the LIP dataset, 2.3% higher than the result reported in the original SCHP paper.

Tech Stack

  1. Languages

    Python, TypeScript, JavaScript, C#, C++, Java, Golang, SQL

  2. Machine Learning

    PyTorch, PyTorch Lightning, TensorFlow, scikit-learn, pandas, MONAI, timm, YOLO11, OpenCV, Grad-CAM, ONNX, TensorRT, Weights & Biases

  3. Generative AI

    LangChain, Hugging Face, RAG, LLMs, Microsoft Copilot

  4. Backend & Web

    FastAPI, .NET, React, Node.js, REST APIs, Gradio, HTML, CSS, Tailwind CSS

  5. Databases

    PostgreSQL, MS SQL Server, Azure SQL, MySQL, MongoDB, Neo4j

  6. Cloud & DevOps

    Azure App Service, Azure DevOps, GCP, Google Cloud Run, Docker, GitHub Actions, CI/CD, Prometheus, Git

Publications

  1. Analyzing the Efficacy of Large Language Models: A Comparative Study

    Presented at the 35th International Conference on Database and Expert Systems Applications (DEXA) 2024, Naples, Italy

Achievements

  1. Graduate Grader, Computational Modeling in Cognitive Science, University of Arizona (Spring 2026).

  2. Specialization in Data Science and Big Data Analytics, Shiv Nadar Institute of Eminence (May 2024).

  3. Dean's List Award as a top 10% performing student at Shiv Nadar Institute of Eminence (Fall 2020, Spring 2021).