Software Engineer / Systems Builder

Aditya Jhaveri builds internal platforms, distributed systems, and practical AI workflows.

Graduate CS engineer turning operational bottlenecks into fast, usable software.

Every Computer Science journey begins with Hello World. Mine began with Age of Empires, which shaped how I think about systems, tradeoffs, and execution. Today I focus on backend-heavy products that improve reliability, scale data pipelines, and make internal teams move faster.

Internal SaaS AI Workflow Automation Distributed Systems Data Platforms
View Selected Work Get In Touch View GitHub
Aditya Jhaveri
systems in orbit
Impact Console Live Snapshot
3B+ Daily records supported across large-scale data workflows
10+ REST endpoints shipped for internal NYU workflow modernization
40% Retry failure reduction after concurrency-safe form delivery work
35% Transfer time improvement from Kafka-based file movement architecture

< About Me />

About Me

I build software that makes complex workflows easier to run.

The orbit map below shows how that focus formed across education, internships, distributed systems work, and internal product delivery.

Career Orbit Map

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Hover a ring or planet to inspect that phase.

< Professional Experience />

Software Developer

New York University
Feb 2025 - Present
Internal SaaS / Ops

Developing workflow software for NYU's Global Enrollment Management and Student Success team, supporting enrollment operations, staff communications, and service processes used by campus administrators.

Role Focus

Partner with EM tech admins to design and improve Google Cloud-based SaaS solutions across the full lifecycle: backend logic analysis, front-end development, documentation, alpha testing, and production optimization.

Impact Highlights

  • Migrated NYU's Asset Management System from Google Apps Script/Sheets toward a TypeScript + Node.js/Express + MongoDB architecture on Hostinger, developing 10+ REST endpoints and enabling real-time sync where none previously existed
  • Engineered a fault-tolerant, concurrency-safe form delivery system, reducing retry failures by 40% during peak submissions
  • Built and owned a placeholder-driven email template platform that auto-populates user, credential, and device data, reducing email management effort by 55%
  • Implemented compound label-based filtering (UI + backend query logic), reducing lookup time across 2,000+ records
  • Improved Google Apps Script retrieval speed by 50% by replacing service-layer calls with direct Google Sheets API access
  • Architected a PoC migrating 1,000+ Google Sheets records to Firestore and Google Cloud SQL for scalability and reliability
  • Built an automated Shipping Label Page that reduced manual processing time by 50%

Stack & Skills

JavaScript TypeScript Google Apps Script Google Sheets API Node.js Express MongoDB Hostinger HTML UI/UX Testing Technical Documentation

Software Development Engineer

Sainapse
July 2022 - May 2024
Data Platforms

Worked in Sainapse's Research, Technology and Platform department, building platform capabilities that supported large-scale data ingestion, transfer, and downstream analytics and ML workflows.

Role Focus

Developed PoCs, solved complex production bugs, and implemented performance-focused platform improvements across microservices, storage systems, and data transfer pipelines on AWS (Linux).

Impact Highlights

  • Optimized microservice file transfers via Apache Kafka using byte-level serialization/deserialization and parallel file distribution, reducing time complexity by 50% and space complexity by 33%
  • Spearheaded HDFS implementation in Java for high-volume ingestion, storing 3B+ daily records with Hive across 10+ microservices and improving average system performance by 37%
  • Designed a batch data adapter attached to BigQuery tables, enabling on-demand transfer of 2B+ rows for downstream ML workflows including free-text search and deduplication
  • Improved extraction speed from 3GB+ XLSX and DOCX files by 30% by updating parsing logic with the Apryse Java SDK
  • Pioneered an Apache Thrift proof of concept for cross-language model development across Java, Python, and Scala
  • Mentored 4 university interns on product architecture and core technology stack, enabling independent feature delivery

Stack & Skills

Java Apache Kafka HDFS Apache Hive AWS (Linux) BigQuery Apryse SDK Apache Thrift Python Scala Data Pipelines PoCs Mentorship

Data Science Intern

AiDash
Jan 2022 - June 2022
GeoAI / Remote Sensing

Supported geospatial ML work at AiDash by developing vegetation classification methods and LANDSAT image classification workflows for remote-sensing use cases.

Role Focus

Built statistical approaches for vegetation classification, trained and evaluated LANDSAT machine learning models, and assessed model quality using metrics such as accuracy, precision, recall, and F1 score.

Impact Highlights

  • Evaluated ResNet architectures with Keras and TensorFlow for LANDSAT classification, achieving 60%-75% accuracy across experiments
  • Devised a Python-based grassland classification method to improve land ranking, achieving 78% accuracy and ranking 1st among peer solutions
  • Built a Python script to automate satellite image labeling in QGIS, reducing manual processing time by 80%
  • Resolved geospatial data handling during onboarding by performing CRUD operations on 10,000+ shapefiles using GeoPandas and PostgreSQL

Stack & Skills

Python TensorFlow Keras ResNet LANDSAT QGIS GeoPandas PostgreSQL Geospatial ML Model Evaluation Precision/Recall/F1 Data Labeling

Data Analyst Intern

PayPal
May 2020 - June 2020
Forecasting / Analytics

Supported demand analytics at PayPal by developing and maintaining forecasting models to identify trends in customer demand behavior.

Role Focus

Built forecasting models, evaluated regression performance across multiple loss functions, and translated findings into a clear presentation with a peer team.

Impact Highlights

  • Identified 10+ customer demand trends and developed forecasting models with R² values close to 0.9
  • Achieved R² scores of 0.98, 0.91, and 0.85 for squared, absolute, and infinite loss functions in a linear regression problem, demonstrating robustness to outliers
  • Collaborated with a team of 4 peers to deliver a comprehensive presentation on forecasting customer demand

Stack & Skills

Python Forecasting Linear Regression Regression Loss Functions R² Evaluation Model Evaluation Data Analysis Presentation

< Featured Projects />

A few selected builds that best represent how I approach product constraints, backend systems, and measurable technical impact.

Selected Build 01
Role Full-stack architecture
Focus Messaging + reminders
Demo Product / Distributed System

Smart Task Manager

Production-style task manager with AI suggestions, saved places, location-aware reminders, and Telegram-first chat actions across a Next.js frontend, ASP.NET Core API, MongoDB, Redis, and Python recommender.

Chat Telegram-first workflow with Linq retained as an optional fallback channel
70% Lower recommendation latency from TF-IDF plus Redis caching
CI/CD Multi-service validation across frontend, .NET API, and recommendation service

What It Solves

Connects web task management with saved places and chat-based quick actions, so users can receive contextual reminders near a linked place and complete or snooze from messaging.

Why It Matters

Demonstrates product-aware architecture: the web app remains the control surface, messaging becomes the action surface, and browser location limits are handled explicitly instead of overclaimed as native geofencing.

What I Built

  • Next.js and TypeScript frontend with authenticated tasks, reusable saved places, browser current-location capture, OpenStreetMap search, and map pin selection
  • ASP.NET Core API with JWT auth, repository/service layers, MongoDB persistence, Redis caching, reminder decisioning, and provider-specific message dispatch
  • Telegram webhook flow for HELP, LIST, ADD, DONE, DONE ALL, DELETE, and SNOOZE commands, with Linq retained as an optional text-based fallback

Engineering Highlights

  • Grouped multiple nearby linked tasks into one reminder and limited triggers to tasks attached to the matched saved place
  • Used temporary reminder context and snooze state so chat replies can complete or defer the correct tasks
  • Kept AI suggestions resilient with TF-IDF, Redis caching, retry-scoped recommender calls, and safe fallback responses
Next.js TypeScript ASP.NET Core MongoDB Redis FastAPI Scikit-learn Telegram Bot API Leaflet OpenStreetMap
View Code Live App
Selected Build 02
Role Backend architecture
Focus Event-driven systems
Assessment Demo / Microservices

Market Data Service

Production-style market ingestion microservice built to show how polling, event streaming, caching, persistence, and analytics-ready APIs fit together cleanly.

40% Higher processing scalability in the final design
25% Lower average API response time
CI Automated tests and Docker validation in GitHub Actions

What It Solves

Demonstrates a real backend architecture for quote polling and derived analytics, instead of a single-script assessment submission.

Why It Matters

Shows API design, async event processing, service orchestration, caching strategy, and testability in one coherent system.

What I Built

  • FastAPI service for polling quotes, moving averages, and latest-price endpoints
  • Kafka publishing and consumption around market events and downstream processing
  • Redis cache plus PostgreSQL raw and processed persistence layers

Engineering Highlights

  • Docker Compose stack for reproducible local setup
  • SlowAPI rate limiting and pytest coverage across sync and async routes
  • GitHub Actions workflow for automated test and build validation
FastAPI Kafka Redis PostgreSQL Docker Pytest
View Code
Selected Build 03
Role End-to-end implementation
Focus AI systems breadth
Coursework / Multi-Assignment

AI Project Suite

A concentrated portfolio of AI coursework turned into working implementations across anomaly detection, computer vision, planning, routing, retrieval, and video-data pipelines.

Multi Planning, CV, anomaly detection, retrieval, and ETL in one suite
Docs Notebook explanations designed for coursework review and discussion
Reuse Practical code structure beyond one-off assignment submissions

What It Solves

Transforms separate lecture topics into concrete systems, making the portfolio useful for technical interviews instead of just classroom grading.

Why It Matters

Shows breadth across both modeling and systems work, which is stronger than showcasing only one narrow AI problem.

What I Built

  • Anomalib workflows with PatchCore and EfficientAD plus related similarity-search work
  • Detection, tracking, and traffic-counting tasks including YOLOv4-CSP and Kalman filters
  • PDDL-based LLM routing paired with OpenRouter for runtime request decisions

Pipeline Highlights

  • Video ETL and featurization pipeline with MongoDB, OpenCLIP, BERT, spaCy, and Qdrant
  • Notebook-backed explanations for statistical modeling and optimization assignments
  • Reusable implementations spanning theory-heavy and systems-heavy AI work
Python Qdrant MongoDB OpenCLIP anomalib PDDL
View Code
Selected Build 04
Role Full-stack workflow
Focus Operational automation
NYU / Workflow Automation

Apple Wallet Coupon System

Admin workflow for creating, distributing, validating, and tracking Apple Wallet coupons across Google Apps Script, PassKit, Google Sheets, email, and SMS channels.

500+ Coupons generated through the automated workflow
40% Operational efficiency improvement from automation
2-3w Assessment delivery timeline under interview constraints

What It Solves

Replaces manual coupon campaign setup and redemption tracking with a repeatable admin workflow and validation-backed redemption logic.

Why It Matters

Combines workflow design, third-party API integration, operational validation, and delivery speed under a realistic interview constraint.

What I Built

  • Campaign creation and management flow in Google Apps Script with configurable coupon settings
  • Coupon distribution via QR, email, and SMS with Twilio and PassKit integration
  • Scanner-assisted redemption validation and Google Sheets-backed campaign records

Execution Highlights

  • Handled duplicate redemption and counter integrity validation in the workflow
  • Added expiry notification automation through self-triggering scripts
  • Built a responsive admin-facing UI for practical operations use
Google Apps Script PassKit Google Sheets Twilio Bootstrap
View Code
Coursework / CV Portfolio

Computer Vision Portfolio

Organized CV portfolio spanning restoration, segmentation, tracking, multimodal Kaggle workflows, and geolocation, with reproducibility notes and reusable scripts.

PyTorch OpenCV CellPose YOLO Albumentations
View Code
Portfolio / Product Layer

Portfolio Chatbot

Embedded recruiter-facing assistant for this site that retrieves context from structured markdown notes and responds with grounded portfolio answers.

JavaScript PHP Markdown Retrieval Gemini API
Open Chat

< Technical Skills />

I work across backend systems, data-heavy services, and internal product delivery. These are the capability areas I reach for most often, rather than an exhaustive inventory of every tool I have touched.

Languages

Core

Primary programming languages I use across systems work, APIs, scripting, and front-end implementation.

Java Python JavaScript (ES6+) C++ C# SQL Bash HTML/CSS TypeScript

Frameworks & Backend

App & API

Frameworks and backend patterns I use for web apps, APIs, service layers, and application architecture.

Spring Boot ASP.NET Core JPA (Hibernate) REST APIs MVC Next.js Express.js FastAPI Swagger Maven Bootstrap Node React

Distributed Systems

Scale

Technologies I have used for messaging, large-scale data processing, and cross-service communication.

Kafka Hadoop Spark Apache Thrift Telegram Bot API Webhook Integrations

Data, ML & Analytics

Modeling

Libraries and data tooling I use for analytics, machine learning, geospatial work, retrieval, and LLM workflows.

TensorFlow KERAS NumPy Pandas SciPy Scikit-Learn GEOPANDAS OpenCV QDRANT Pig Hive UNSLOTH LangChain

Cloud, DevOps & Tools

Delivery

Cloud platforms, storage systems, testing, and workflow tools I use for shipping and maintaining software.

AWS GCP BIGQUERY PostgreSQL MySQL MongoDB Redis FIRESTORE Docker GitHub Actions Vercel Version Control (Git, CI/CD) Postman JIRA IntelliJ PyCharm JUPYTER Notebook Visual Studio Code QGIS JUnit PYTEST Agile NoSQL Cassandra

< Education />

Master of Science in Computer Science

New York University - Tandon School of Engineering

Sep 2024 - May 2026
Graduate
NYU Tandon Computer Science New York, USA

Coursework focus: Algorithms, Big Data, Options Pricing and Stochastic Calculus, Search Engines.

Bachelor of Engineering in Computer Science

Birla Institute of Technology and Science, Pilani

Aug 2018 - July 2022 | Minor in Data Science
Undergraduate
BITS Pilani Computer Science Minor: Data Science

Coursework focus: Data Mining, Deep Learning, Machine Learning, Natural Language Processing.

< Resume />

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open docs/resume.pdf · updated 2026

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< Let's Build Something />

If you want to talk through backend systems, internal tools, AI-enabled workflows, or a role where I can help ship reliable software, reach out directly or send a note below.

[@] [email protected]
[>>] LinkedIn
[</>] GitHub
[*] Jersey City, NJ

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