Machine Learning Software Engineer

Aryan Rupesh Solanki

Dallas, TX ***** 469-***-**** ****************@*****.*** linkedin.com/in/aryan-solanki-312001/ EDUCATION

Master of Science, Computer Science GPA: 3.56

The University of Texas at Dallas, Richardson TX May 2025 Coursework: Design and Analysis of Computer Algorithms, Database Design, Big Data Management, Statistical Method Bachelor of Technology, Electronics Engineering GPA: 9.2/10 Sardar Patel Institute of Technology, India May 2023 Minor in Computer Engineering

Coursework: Structured Programming, Machine Learning, Artificial Intelligence, Operating Systems, Data Structure TECHNICAL SKILLS

• Languages and Frameworks: Python, Java, JavaScript, Typescript, React.js, Node.js, Express.js, Flask, Django

• Database: SQL (MySQL, PostgreSQL), NoSQL (MongoDB, GCP Cloud Firestore)

• Tools/Technology: JIRA, Jenkins, GitLab, Linux, GitHub, Docker, Postman, Elasticsearch, Kibana, Logstash

• Cloud: GCP (Kubernetes, Firestore, IAM), AWS (S3, RDS, EC2, ECS, Route53, Lambda, API gateway, RDS, IAM)

• Development Practices: Agile, Scrum, DevOps, Continuous Integration/Continuous Deployment (CI/CD) PROFESSIONAL EXPERIENCE

Software Engineer Intern – Cloud & Automation, Phoenix Infotech Pvt Ltd, Mumbai, India August 2022 – June 2023

• Automated file structuring by integrating AWS S3, Lambda, IAM, and Step Functions, reducing document retrieval time by 40%, improving data organization, and enforcing access controls for security.

• Designed and built a high-performance report processing pipeline using AWS DynamoDB, Lambda, SQS, CloudWatch, and Step Functions, accelerating data handling by 50% while ensuring real-time monitoring, scalability, and fault tolerance.

• Developed a fully scalable headless CMS leveraging AWS API Gateway, S3, CloudFront, RDS, and CloudTrail, handling 100K+ daily requests, optimizing content delivery, and improving data consistency.

• Implemented CI/CD pipelines using AWS CodePipeline, CloudFormation, ECS, and CloudTrail, automating deployments, reducing downtime by 60%, and streamlining containerized application management with auditing for security compliance. Software Development Intern, Autobuddys, Mumbai, India January 2022 – July 2022

• Developed a scalable, modular mobile application using GCP Firebase, Flutter and Dart, adhering to agile development practices and achieving over 200 downloads.

• Designed and implemented a game with adaptive difficulty using object-oriented programming (OOP) principles, leading to an 18% improvement in learning outcomes for children with autism.

• Spearheaded the launch of a children’s application with real-time data synchronization and progress tracking dashboard, increasing user engagement by 30%.

ACADEMIC PROJECTS

Realtime Chat App with Sentiment Analysis, UT Dallas February 2024 – March 2024

• Designed a real-time chat app with sentiment analysis using Next.js and Pusher, enhancing user interaction and experience.

• Integrated sentiment-aware communication features, leveraging RESTful APIs for seamless data exchange and improving conversation quality.

Full-Stack Food Ordering Platform, UT Dallas November 2023 – January 2024

• Built a full-stack food ordering platform using the MERN stack and TypeScript, optimizing for scalability and user experience.

• Implemented CRUD operation using REST API, Postman to enhance the platform's data retrieval capabilities.

• Integrated Stripe for secure payment processing and utilized API integrations for real-time order tracking and operational efficiency.

Library Management system, UT Dallas August 2023 – October 2023

• Led design and development of a Library Management System, applying software engineering principles to automate operations.

• Devised robust relational database schema using SQL and normalization techniques, ensuring efficient data storage and retrieval.

• Implemented reporting tools for data analysis and resource optimization, enabling strategic decision-making. Deep Reinforcement Learning based Intelligent Traffic Control June 2022 – June 2023

• Developed and deployed Deep Reinforcement Learning (DRL) algorithms in Python to optimize traffic flow at intersections, reducing wait times by 6-21% compared to traditional approaches.

• Utilized TensorFlow and Keras for model training and evaluation, improving computational efficiency and scalability. PUBLICATION

• Deep Reinforcement Learning based Intelligent Traffic Control (link) [VQGAN, TensorFlow, Keras, Python]: IEEE

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