Hi, my name is

Aarush Narang

I build  

Data scientist and ML engineer. At HCLTech I shipped a Gen AI copilot that cut ticket resolution time 76% (105→25 min) and reduced daily service desk tickets 26% across 4,000+ knowledge files. Currently pursuing an MS in Data Science at the University of Arizona.

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About Me

Aarush Narang

I'm a data scientist and ML engineer with two years of industry experience shipping production AI systems. At HCLTech I built Gen AI copilots with LangChain and Hugging Face over 4,000+ enterprise knowledge files, and deployed cost models on Azure behind Docker and CI/CD; earlier at Carrier and PwC I shipped ML-driven tooling and graph data pipelines that measurably cut cost and turnaround.

I'm now pursuing an MS in Data Science at the University of Arizona (2025–2027, GPA 3.83), after a B.Tech in Computer Science from Shiv Nadar University. I care about turning messy data into scalable, measurable decision-making systems.

Languages
PythonTypeScriptJavaScriptC#C++JavaGolangSQL
Machine Learning
PyTorchPyTorch LightningTensorFlowscikit-learnpandasMONAItimmYOLO11OpenCVGrad-CAMONNXTensorRTWeights & Biases
Generative AI
LangChainHugging FaceRAGLLMsMicrosoft Copilot
Backend & Web
FastAPI.NETReactNode.jsREST APIsGradioHTMLCSSTailwind CSS
Databases
PostgreSQLMS SQL ServerAzure SQLMySQLMongoDBNeo4j
Cloud & DevOps
Azure App ServiceAzure DevOpsGCPCloud RunDockerGitHub ActionsCI/CDPrometheusGit

Experience

Software Engineer · HCLTech

Jul 2024 – Jul 2025

Generative AI Development Team

  • 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 26% (2,300→1,700) and resolution time 76% (105→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.
PythonTypeScriptLangChainHugging FaceFastAPIAzureDocker

Software Engineer Intern · HCLTech

Jan 2024 – Jul 2024

Sales KPP Enterprise Application

  • Developed the Sales KPP enterprise application end to end with React, C#, .NET & 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.
ReactC#.NETMS SQL Server

Web Developer Intern · Carrier

May 2023 – Jul 2023
  • Designed and developed the Account Payable Management System and Ticket Tracker for the sales team using HTML, CSS, JavaScript, jQuery & jQWidgets.
  • Integrated an FAQ chatbot that cut support queries 38% (450→280 per week), and added automated tests.
JavaScriptjQueryjQWidgetsHTMLCSS

Technical Intern · PwC

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.
PythonNeo4jGolangREST APIsDocker

Featured Projects

Reflex detecting and tracking road users at an intersection, with risk lines between vehicles and pedestrians

Reflex — Collision Risk Detection

94.4% vehicle detection F1, validated against the Urban Tracker benchmark

Scans traffic video and flags dangerous close calls between cars, cyclists, and pedestrians. Uses YOLO11, ByteTrack, and OpenCV to detect and track road users, flags near misses at 87.6% precision against hand labeled events, and is optimized with TensorRT to cut processing time 3.8x (12→46 fps) for live feeds. Ships a dashboard that replays flagged events and maps where close calls cluster to reveal a city's riskiest intersections.

PythonPyTorchYOLO11OpenCVFastAPIReactPostgreSQL
Code
Chest X-ray from the NIH ChestX-ray14 dataset used by RadarMD

RadarMD — Chest X-ray Triage

0.84 mean AUROC across 14 thoracic pathologies — a 6 point gain over baseline

Deep learning triage system for chest X-rays, built on NIH ChestX-ray14 (112,000+ images, 30,000 patients). Trains DenseNet-121 and ConvNeXt with PyTorch Lightning under strict patient-level splits, reaching 0.84 mean AUROC across 60+ Weights & Biases experiments. Grad-CAM localization validated against 880 ground truth bounding boxes, with a test suite prioritizing serious findings over raw accuracy to keep missed diagnoses under 8%. Deployed via ONNX export for 4x faster CPU inference (~120 ms/image), serving a Gradio app on Cloud Run with GitHub Actions CI/CD and Prometheus metrics.

PyTorchMONAItimmGrad-CAMONNXFastAPIDockerGCP
Code
Human parsing model output

Self-Correction for Human Parsing

84.7% mIoU on LIP — 2.3% above the published SCHP baseline

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.

PythonPyTorchComputer Vision
Code
Publication · DEXA 2024 · Naples, Italy

Analyzing the Efficacy of Large Language Models: A Comparative Study

Peer-reviewed paper (co-authored) presented at the 35th International Conference on Database and Expert Systems Applications, published in the Springer LNCS series.

Read Paper

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