Hi, I'm Aryan Patodiya

AIML Developer

Building impactful, scalable AI solutions with expertise in machine learning, deep learning, and cloud infrastructure.

Aryan Patodiya

About Me

Aryan Patodiya
1.5+ Years
Experience

Graduate Computer Science student with ~1.5 years of hands-on experience through research, internships, and startup work in machine learning, backend development, and cloud infrastructure.

Built end-to-end ML pipelines at ISRO, deployed real-time systems using AWS and Azure, and developed LLM-based applications for document search and forecasting. Skilled in Python, C++, deep learning frameworks, and MLOps tools.

Eager to contribute to impactful, scalable AI solutions that solve real-world problems and drive innovation.

Education

MS in Computer Science

Experience

ISRO, Raven Technolabs, NanoTech Technologies

Projects

ML/AI Applications

Download Resume

Skills & Expertise

Programming

Python

Python

Primary language for ML/AI development

Java

Java

Backend development & enterprise applications

C++

C++

Performance-critical applications & algorithms

SQL

SQL

Database querying & management

JavaScript

JavaScript

Web development & interactive applications

TypeScript

TypeScript

Type-safe JavaScript development

Machine Learning & Deep Learning

PyTorch

PyTorch

Deep learning research & development

TensorFlow

TensorFlow

Production ML systems & deployment

OpenCV

OpenCV

Computer vision applications

Transformers

Transformers

NLP models & applications

GPT

GPT

Large language model applications

CNNs

CNNs

Computer vision & image processing

RNNs

RNNs

Sequence modeling & time series

LLMs

LLMs

Large language model development

RL

Reinforcement Learning

Agent-based learning & optimization

CUDA

CUDA

GPU acceleration for deep learning

Model Serving & Optimization

Triton

Triton Inference Server

High-performance model serving

vLLM

vLLM

High-throughput LLM inference

ONNX

ONNX

Model interoperability & exchange

TensorRT

TensorRT

High-performance deep learning inference

TorchScript

TorchScript

PyTorch model optimization

Cloud & MLOps

AWS

AWS

Cloud infrastructure & services

Azure

Azure

Microsoft cloud platform

Docker

Docker

Containerization & deployment

Kubernetes

Kubernetes

Container orchestration

Terraform

Terraform

Infrastructure as code

CI/CD

CI/CD

Continuous integration & deployment

MLflow

MLflow

ML lifecycle management

Data Engineering & Big Data

Kafka

Apache Kafka

Distributed event streaming

Hadoop

Hadoop

Distributed data processing

MongoDB

MongoDB

NoSQL database

Redis

Redis

In-memory data store

MySQL

MySQL

Relational database

PostgreSQL

PostgreSQL

Advanced relational database

Software Development

OOP

Object-Oriented Programming

Software design & architecture

Design Patterns

Design Patterns

Reusable software solutions

REST & GraphQL

REST & GraphQL APIs

API design & implementation

Multi-threading

Multi-threading

Concurrent programming

Agile

Agile Methodologies

Scrum, Kanban, & project management

Work Experience

SAC-Indian Space Research Organization

ML Intern – Hydrological Modeling & Forecasting

Dec 2022 - Apr 2023

  • Built a forecasting pipeline using HMMs, Markov Chains, and LDA to model rainfall patterns; improved prediction accuracy by ~20%.
  • Processed and cleaned hundreds of GBs of satellite data using Pandas and AWS S3, reducing prep time by ~30%.
  • Integrated results into flood risk tools, helping improve early warning reliability across pilot regions.

Raven Technolabs

ML Engineering Intern – Cloud Infrastructure & Model Deployment

May 2022 - July 2022

  • Developed backend microservices in Spring Boot and Node.js for real-time ML model deployment.
  • Created REST/GraphQL APIs to support model inference; improved latency by ~25%.
  • Automated CI/CD pipelines using GitHub Actions and AWS CodeDeploy, reducing manual deployments by 50%.

Nanotech Technologies

Cofounder & Lead Software Engineer – Scalable Systems & Data Engineering

Mar 2019 - Nov 2021

  • Led a 14-member team to build scalable edge-to-cloud data pipelines for industrial automation.
  • Optimized backend systems via low-level code refactoring and DB tuning, improving performance by ~20%.
  • Built infrastructure for real-time monitoring and enabled future ML use cases like predictive maintenance.

California State University

Brain-Computer Interface Research Assistant

June 2025 - Present

  • Built and optimized a 2D CNN for EEG signal classification in MATLAB, incorporating focal loss and cosine learning rate scheduling to address class imbalance and enhance generalization.
  • Developed a complete preprocessing pipeline to convert raw EEG signals into structured formats suitable for deep learning, including spatial mapping and 3-way stratified splitting.
  • Currently extending the framework to 3D CNNs using tensor-based EEG modeling and imputation strategies, aiming to capture spatio-temporal dynamics for improved classification performance.

Projects

Explore my latest work in AI/ML and software development

MarketPulse: Real-Time Stock Trend Predictor

LSTM GRU Streamlit

Built and trained LSTM/GRU models on OHLCV data; achieved ~80% directional accuracy across selected stocks. Developed a Streamlit-based dashboard for real-time trend prediction; reduced update latency by ~25%.

DocuQuery: Fullstack Semantic Search Engine

LLaMA 2 LangChain FAISS

Developed a context-aware document query system using LLaMA 2, LangChain, and FAISS. Integrated OpenAI embeddings to improve retrieval; increased relevance of top results by ~30%. Reduced average query latency through prompt and vector optimization.

TimeNet: Sequence Forecasting with RNNs

RNN TensorFlow Time Series

Designed RNN models for rainfall and energy consumption forecasting using historical data. Streamlined data pipelines with TensorFlow, reducing training time by ~20%. Validated performance against ARIMA models, achieving ~15–20% higher precision.

Optimizing RAG with Reinforcement Learning

PPO RAG SQuAD/NQ

Building a custom PPO-based framework to enhance document retrieval in RAG systems. Evaluating retrieval relevance using BLEU and Exact Match on SQuAD/NQ datasets. Implementing a novel reward function that balances relevance and diversity.

SkyDefender: 3D Shooter & Duck Hunt Game

C++ OpenGL GLUT Game Engine SOIL irrKlang

Wrote a full 3D game engine in C++ with OpenGL/GLUT, featuring skybox rendering, parallax environments, and multi-level progression. Implemented drone pathing and probabilistic duck spawning with integrated collision detection. Optimized graphics/audio subsystems (SOIL, irrKlang) to sustain ~60 FPS across complex scenes.

Education

California State University, Fresno

Master of Science in Computer Science

Jan 2024 - Present | GPA: 3.9/4.0

Relevant Coursework: Advanced Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, Distributed Systems

Charusat University

Bachelor of Technology in Computer Science and Engineering

July 2019 - Apr 2023 | GPA: 3.82/4.0

Relevant Coursework: Data Structures and Algorithms, Operating Systems, Database Management Systems, Computer Networks, Artificial Intelligence, Machine Learning

Contact Me

Let's discuss how we can work together

Email

aryanpatodiya018@gmail.com

Phone

+1 (925)-918-1058

Location

Fresno, CA