Abhinav Pinisetti
Incoming Software Engineering Intern @ CACI International Inc
About
Hey! I'm Abhinav and I’m a Computer Science student at the Georgia Institute of Technology, passionate about machine learning, systems, databases, and infrastructure optimization.I’m especially interested in how robust backend systems and secure infrastructure can power real-world ML applications, from optimizing performance at scale to designing models that are both efficient and reliable.Feel free to reach out at apinisetti6@gatech.edu, I'd love to chat!
United States
Washington DC-Baltimore Area
Computer Software
TensorFlow, Keras, Natural Language Processing (NLP), Large Language Models (LLM), React.js, Web Design, Web Development, Communication, Swift (Programming Language), Artificial Intelligence (AI), Convolutional Neural Networks (CNN), Teamwork, Coaching, Time Management, Team Management, Leadership, Swim Instruction, Cascading Style Sheets (CSS), Python (Programming Language), HTML5
Experience

Undergraduate Researcher
Atlanta, GA
• Developed ML-driven GLOSA algorithms in Python, integrating SPaT, queue estimates, and multimodal sensor data,reducing intersection delay by 12–18% • Built LLM/VLM-driven scene understanding pipelines, improving edge-case detection by 20% across 10+ urban scenarios • Ran large-scale SUMO and CARLA simulations (100+ traffic seeds), achieving 10–15% travel time reduction and 8–12% fuel efficiency gains

Firmware Engineer
Atlanta, GA
• Programmed embedded firmware in C++ for Teensy 4.1 microcontrollers using PlatformIO and VS Code, supporting real-time control loops at 1–5 kHz • Implemented CI/CD-backed unit testing for pedal system firmware, increasing test coverage to 85%+ and reducing integration bugs by 30% • Utilized a real-time TCP communication layer between distributed microcontrollers, enabling <10 ms diagnostic latency and reliable fail-safe shutdown signaling

Cybersecurity Intern
Reston, VA
• Reported 15+ critical vulnerabilities in ThreatResponder to the CEO, improving total system stability & efficiency • Optimized threat analysis workflows and reduced runtime by 30% for faster incident response and detailed analysis • Enhanced the malware detection pipeline, improving overall model accuracy by 12% through the integration of ML
Abhinav Pinisetti's Contact Information
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