Anirudh Sunil

Software Developer

Creating innovative solutions with experience in backend development, cloud technologies, hardware, and AI. Seeking internships as an undergraduate student at UIUC.

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About Me

I'm a passionate software engineer with experience in cloud technologies and hardware integration. My journey in tech has equipped me with a diverse skill set spanning from backend development to cloud infrastructure management.

With a strong foundation in computer science and hands-on experience through internships, I bring a unique perspective to solving complex technical challenges. I'm currently pursuing my undergraduate degree at UIUC studying Mathematics and Computer Science.

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Work Experience

Incoming Software Engineering Intern

Warmly

June 2025-Current

Software Engineering Intern

Venu AI

YC W21

May 2025 - June 2025

Implemented authentication for user access to the dashboard feature, enhancing security and user experience
Designed Toastify notification feature for CSV processing and email generation, improving user awareness and workflow efficiency

Software and Systems Development Intern

Trydan Tech

Summer 2024

Leveraged CAN communication protocols in Python using cantools modules to collect data for the EV scooter division XSTRAD, ensuring accurate testing with Synchronous Reluctance motor via Raspberry Pi's SPI interface and 2CH CAN FD HAT, which improved data accuracy and testing efficiency
Decoded bits from a DBC file and organized data in a CSV format, facilitating effective analysis and identification of correlations among parameters such as speed, temperature, and phase current, which enhanced data-driven decision-making
Collaborated with peers to analyze 10,000 data points across 26 motor variables, improving code review and quality assurance practices through detailed analysis and teamwork, which led to enhanced software reliability and performance

Software Engineering Intern - Cloud Security

Prancer.io

Summer 2022

Debugged Amazon Web Services S3 buckets and permissions to organize configuration layout, improving data access efficiency
Reconfigured Kubernetes pods to optimize resource allocation, enhancing the performance of pod clusters, containers, and nodes
Established secure connections to regulate SQL databases, successfully preventing injection attacks and ensuring data integrity
Utilized Shift Left Security tools such as Burp Suite proxy, OWASP ZAP, and DirBuster to secure websites, reducing vulnerabilities and enhancing security posture
Enhanced understanding of infrastructure, cloud offerings, and DevOps as code concepts, leading to the development of more effective cloud security solutions

Projects

Rankd

Built using Supabase, NextJS, and Tailwind. Used ELO algorithm to calculate rankings for companies and characters for leaderboard display. Currently has 150+ users and more than 500+ games played.

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LLMJudge

Developed a web app with Firebase, TypeScript, and Tailwind CSS that benchmarks prompts and LLM outputs on length and tone. Evaluated Gemini 2.0 Flash output with Perplexity Sonar, R1-1776, and Llama 3 with API calls and optimized. Stored data for prior prompts and scores in CSV and visualized overall evaluation scores with bar chart.

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PoseMind AI: Contextual Yoga Recommender

Developed a contextual Yoga Pose recommender app using Firestore, Vector Search, Langchain, and Gemini to deliver personalized pose suggestions based on user input. Implemented vector search and AI-generated embeddings to enable natural language querying and efficient retrieval of yoga pose recommendations. Designed and deployed a user-friendly web application with AI capabilities, integrating text, images, and audio for an enhanced user experience.

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Medicina.ai

Designed web app with Python and Flask to update heart disease risk using linear regression on user input data. Stores data in SQL database for login data and local user data and continuously updates visual chart to display user risk for diabetes and heart disease. Implemented LLM chatbot feature using Gemini API for real-time communication with user.

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Quantitative Analysis of Protein Structure

Worked with Chad Rienstra Lab to develop an automated tool to compare experimental and simulated SSNMR EmRe protein data with ZNCC scores using Python and Bash scripts.

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