Software Developer Intern

AKSHATA SHINDE

*****************@*****.*** +1-201-***-**** LinkedIn GitHub

TECHNICAL SKILLS

Languages: Python, JavaScript, TypeScript, Java

ML & AI: TensorFlow, PyTorch, Scikit-learn, Random Forest, OpenCV ML Lifecycle: Model development, evaluation, optimization, deployment, monitoring Domains: Recommendation Systems, Classification, Optimization, Pattern Recognition, Data Mining, AI Cloud & Distributed Systems: AWS (EC2, S3, Redshift), Kubernetes (GKE), Docker, Heroku Data & Pipelines: ETL, Pandas, NumPy, SQL, MongoDB, PostgreSQL DevOps & Tools: Jenkins, GitHub Actions, Docker, Git, JIRA, Postman, Selenium Frontend: React.js, Redux, Next.js, Bootstrap

Practices: Agile (Scrum), CI/CD, Accessibility (WCAG), Security & Compliance PROFESSIONAL EXPERIENCE

Software Developer Intern

Minds Beyond Measure (NGO) New York Feb 2025 – Apr 2025

• Built and maintained full-stack web apps using MERN, Docker, and MongoDB, delivering real-time user features and boosting engagement by 30%.

• Designed scalable backend architecture and RESTful APIs using OOP principles, ensuring clean code, maintainability, and performance.

• Automated CI/CD workflows with GitHub Actions and Jenkins for smooth, reliable live deployments across environments.

• Applied unit testing, version control (Git), and Agile collaboration to drive ownership, responsiveness, and system stability. Software Developer Intern (ML & Backend)

Trivia Softwares Mumbai Aug 2023 – Dec 2023

• Developed AI-driven recommendation systems and data regression models using PyTorch and TensorFlow, driving a 28% increase in user engagement.

• Integrated ML models into production APIs, serving 100K+ daily requests with high reliability and efficiency.

• Automated ETL and data mining pipelines, enabling real-time insights dashboards for 5 business teams, influencing business recommendations.

Software Engineer Intern– ML Systems & Performance LabMentix India Jan 2022 –Jun 2022

• Optimized PyTorch based neural network training workflows across distributed GPU clusters, improving throughput by 20%.

• Scaled classification, recommendation, and pattern recognition models by addressing system bottlenecks and enhancing AI workload performance.

• Collaborated with hardware and ML infrastructure teams to optimize parallel compute architectures, enabling efficient ML lifecycle operations and high-reliability deployments. PROJECTS

DineDash – Full Stack Food Delivery Platform Capstone Project Feb 2025 – May 2025

• Spearheaded the development team to deploy a scalable food delivery app using React.js, Django REST, and PostgreSQL, enabling secure order tracking for 500+ users.

• Automated CI/CD with Jenkins and AWS EC2, reducing deployment time by 60% and ensuring cloud-ready, microservices architecture.

• Integrated responsive design, streamlined APIs, and caching mechanisms, improving performance, reliability, and user experience across devices

Disease Prediction App (AI SaaS) Sept 2024- Dec 2024

• Developed a disease prediction SaaS app using TensorFlow, Streamlit, and Flask, serving 500+ users.

• Reduced inference time by 35% and improved model accuracy by 12% through optimized ML model serving in Docker on AWS EC2.

Machine Learning Engineer (Project)-Credit Card Fraud Detection Systems Apr 2023 – Jun 2023

• Built scalable fraud detection classifiers using TensorFlow, achieving 92% accuracy on 20K+ transactions.

• Managed end-to-end ML lifecycle, including feature engineering, model training, evaluation, optimization, and API deployment via Flask.

• Deployed services on AWS EC2 using Docker, reducing inference latency by 50% and ensuring 99.9% uptime. EDUCATION

Pace University, Seidenberg School of Computer Science and Information Systems New York, NY Master of Science (MS) in Computer Science GPA: 3.86 Sept 2023 - May 2025 Mumbai University Maharashtra, India

Bachelor of Engineering (BE) in Computer Engineering GPA: 3.65 Aug 2019 - May 2023

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