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naitikshah1008/README.md

Naitik Shah

Software Engineer · Backend & Distributed Systems

What I Solve

I build backend systems where performance and reliability matter.

  • Design APIs and services that handle real-world production workflows
  • Work with distributed systems and streaming pipelines (Kafka)
  • Improve performance, reduce failures, and make systems more stable

Systems I've Built

Backend Systems (Production)

  • Built microservices using Java + Spring Boot, improving API latency by 30%
  • Designed async workflows and validation pipelines reducing failures by 40%
  • Worked on data-heavy systems with structured processing and high-volume transactions

Real-Time & Data Systems

  • Developed streaming pipelines using Kafka + PyFlink
  • Implemented anomaly detection using EWMA and statistical methods
  • Designed systems for low-latency processing and real-time insights

AI-Driven Workflows

  • Built an AI-based scheduling system using LLMs + FastAPI
  • Designed modular architecture for inference and workflow execution
  • Integrated external APIs (Google Calendar) with secure OAuth

Tech Stack

Backend:      Java, Spring Boot, REST APIs, Microservices
Data:         Kafka, PyFlink, ETL, Redis
Databases:    PostgreSQL, MySQL, MongoDB, TimescaleDB
Frontend:     React, Angular, TypeScript
Cloud/DevOps: AWS, Docker, Kubernetes, Jenkins, Linux
Languages:    Java, Python, JavaScript, Go, SQL

Featured Projects

Real-Time System Monitoring

Kafka · PyFlink · PostgreSQL · Docker

Built a distributed system to process and analyze streaming system metrics in real time.
Implemented anomaly detection (EWMA, z-score) and enabled low-latency querying with scalable data pipelines.

CalSync AI - Smart Scheduling System

Go · Python · FastAPI · Docker · LLM

Developed an AI-based system that generates structured schedules from user inputs and integrates with Google Calendar APIs.
Designed a modular architecture with backend services and an LLM orchestration layer for real-time decision workflows.

By The Numbers

  • Reduced system failures by 40% through async processing and validation improvements
  • Improved API performance and latency by 30% in production systems
  • Built systems handling high-volume workflows and real-time data processing

Connect

📫 naitikshah1812@gmail.com
🔗 https://www.linkedin.com/in/naitik1008
💻 https://github.com/naitikshah1008

Pinned Loading

  1. CalSync-AI CalSync-AI Public

    CalSync AI turns learning goals into structured plans and conflict-free schedules synced with Google Calendar using a local LLM.

    JavaScript

  2. Live-Sports-Intelligence Live-Sports-Intelligence Public

    Real-time sports video intelligence system that detects key plays and generates highlight clips using OCR, audio signals, and Kafka.

    Python

  3. Real-Time-System-Monitoring Real-Time-System-Monitoring Public

    Real-time monitoring pipeline using Kafka, Flink, PostgreSQL, and Grafana to stream metrics, detect anomalies (EWMA + 3σ), and visualize results.

    Python 3

  4. MyPortfolio MyPortfolio Public

    MERN-based developer portfolio with an admin dashboard for managing projects, skills, and experience dynamically without code changes.

    JavaScript

  5. Generating-Code-From-A-Graphical-User-Interface-Screenshot Generating-Code-From-A-Graphical-User-Interface-Screenshot Public

    PyTorch implementation of pix2code that converts GUI screenshots into DSL and generates HTML, iOS, or Android UI code.

    Jupyter Notebook

  6. Deep-Learning-for-Sentiment-Classification Deep-Learning-for-Sentiment-Classification Public

    Built a sentiment classifier using Word2Vec embeddings and an averaged perceptron, improving accuracy over sparse features.

    Jupyter Notebook