rahulkotargasthi rio767

🧠 About Me

engineer:
  name: "Rahul K"
  role: "Software Engineer | AI/ML Engineer | Full Stack Developer"
  focus:
    - Designing scalable backend systems with Java & Spring Boot
    - Building production-grade Machine Learning & Deep Learning pipelines
    - Engineering full stack applications with the MERN ecosystem
    - Contributing to high-impact open source projects
  philosophy: "Engineering reliable, data-driven systems with a product mindset"

I'm a Computer Science Engineer with a strong foundation in data structures, algorithms, and system design, combined with hands-on experience across machine learning, full stack development, and cloud-native engineering. My work spans building CNN-based computer vision pipelines for real-world infrastructure problems, optimizing RESTful backend services under simulated production load, and contributing to large-scale open source codebases used by millions of developers worldwide.

I approach engineering with a product mindset — prioritizing performance, security, and measurable impact over surface-level implementation. Whether it's reducing API latency, improving model classification accuracy, or resolving inconsistencies in distributed deployment systems, I focus on outcomes that scale.

🎯 Open To: Software Engineering Roles · Machine Learning Engineering · Full Stack Development · Backend Engineering · Research Collaborations · Open Source Contributions

🛠️ Tech Stack

Languages

Frontend

Backend & Databases

Cloud, DevOps & Tooling

🤖 AI / ML Expertise

Domain	Proficiency	Details
Supervised Learning	⭐⭐⭐⭐☆	Classification & regression pipelines, model evaluation via precision/recall
Convolutional Neural Networks (CNN)	⭐⭐⭐⭐⭐	Satellite imagery classification, geospatial feature extraction, 95% accuracy
Artificial Neural Networks (ANN)	⭐⭐⭐⭐☆	Intrusion detection on network traffic, ensemble autoencoders
Model Evaluation & Tuning	⭐⭐⭐⭐☆	Hyperparameter tuning, feature scaling, dimensionality reduction
Frameworks	⭐⭐⭐⭐☆	TensorFlow, PyTorch, OpenCV
Anomaly Detection	⭐⭐⭐⭐☆	Zero-day attack identification, false-positive reduction

🚀 Featured Projects

🛡️ AI-Powered Intrusion Detection System

Developed an intrusion detection system trained on real-world network traffic data, combining ANN architectures with ensemble autoencoders to identify malicious activity, including previously unseen zero-day attacks.

Attribute	Detail
Stack	Python, Machine Learning, ANN, Autoencoders
Scale	~50,000+ network traffic records
Performance	20% improvement in malicious traffic detection accuracy
Security	Zero-day attack identification via anomaly detection
Impact	15% reduction in false positives, improved real-time threat reliability
Repository	github.com/rio767/intrusion-detection-system

Applied feature scaling, dimensionality reduction, and anomaly detection techniques to build a system capable of identifying novel attack patterns without relying solely on signature-based detection, significantly enhancing real-time network security posture.

⚡ Satellite Imagery Analysis for EV Charging Hubs

Built a CNN-based satellite imagery analysis pipeline to identify optimal locations for EV charging infrastructure by analyzing commercial zoning and traffic pattern data at scale.

Attribute	Detail
Stack	Java, Python, TensorFlow, OpenCV
Scale	~10,000+ satellite images processed
Performance	95% classification accuracy
Security	N/A — Geospatial infrastructure analysis pipeline
Impact	Enabled data-driven EV infrastructure planning decisions
Repository	github.com/rio767/ev-charging-site-classifier

Engineered a full preprocessing, augmentation, and hyperparameter tuning pipeline in TensorFlow to transform raw satellite imagery into actionable infrastructure recommendations, supporting precision/recall-based model validation for production reliability.

💼 Experience

Data Science Intern · Vision Astraa EV Academy

Feb 2026 – May 2026 · Bangalore, India

Developed a CNN-based EV charging site identification model from the ground up, processing large-scale satellite imagery datasets to support infrastructure planning decisions through quantitative model evaluation.

Designed and trained a CNN model using TensorFlow/PyTorch on ~10,000+ satellite images
Implemented preprocessing and geospatial analysis pipelines for raw image data
Conducted hyperparameter tuning to achieve 95% classification accuracy
Delivered precision/recall-based model evaluation for production-readiness validation

Python Java TensorFlow PyTorch Computer Vision Geospatial Analysis

MERN Stack Developer Intern · EDUNET Foundation & EY GDS

Mar 2025 – Apr 2025 · Remote

Built and deployed a scalable full stack web application, focusing on backend efficiency and frontend performance under simulated production-level traffic.

Engineered RESTful APIs with MongoDB indexing and Express middleware
Reduced API latency by 30% under simulated multi-user load (~1K+ requests/day)
Optimized React frontend performance using hooks and state management
Increased user retention by 15% through behavioral session data analysis

MongoDB Express.js React Node.js REST APIs Performance Optimization

🏆 Achievements

Recognition	Details
🌍 Open Source — freeCodeCamp	Resolved UI defect & optimized navigation in a 400K+ star codebase serving 1M+ monthly users
📦 Open Source — PyPA pip	Investigated package distribution resolution, aligned `--no-binary` behavior with pip 23.1
☸️ Open Source — Spinnaker	Resolved Kubernetes deployment inconsistencies in continuous delivery design system
🏁 HackToFuture 2025	Built a full-stack prototype under competitive hackathon constraints
🤖 Google Agentic AI 2025	Developed an AI prototype using Python, TensorFlow, and LangChain
🚀 Bharatiya Antariksh 2025	Engineered ISRO-aligned space-tech solution prototype

📜 Certifications

AWS

IBM

Deloitte

GeeksforGeeks

💻 Coding Profiles

📈 Contribution Activity

🐍 Contribution lifeline

🎯 Current Focus

current_focus:
  learning:
    - Advanced System Design & Distributed Architecture
    - Deep Learning Model Optimization & Deployment (MLOps)
    - Cloud-Native Backend Engineering on AWS
  building:
    - Production-grade Spring Boot microservices
    - End-to-end ML pipelines for real-world infrastructure problems
  exploring:
    - Generative AI & Agentic Workflows (LangChain)
    - Kubernetes-based continuous delivery systems
  open_to:
    - Software Engineering Roles
    - Machine Learning Engineering Roles
    - Full Stack Development Opportunities
    - Open Source Collaborations

📬 Connect With Me

"Engineering is the art of turning data into decisions and code into impact."

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