People Tech Technology Private Limited
Model conversion, runtime behavior, and embedded validation.
National-level problem solving performance with strong engineering intent.
About Me
I’m a Computer Science undergraduate currently working as an AI Developer Intern at PeopleTech Group, where I’m gaining hands-on experience with real-world AI and ML solutions. My journey has been shaped by solving practical problems, writing efficient code, and understanding how reliable systems are built. Over time, my interests have grown toward Artificial Intelligence, backend systems, and scalable software engineering. I’m especially interested in building, optimizing, and deploying solutions that are not only functional, but efficient, dependable, and ready to perform in real environments. I’m targeting high-impact backend or SDE roles in product-driven companies where ownership, engineering quality, and scalable thinking matter.
Education & Experience
Education
2022 - 2026
Vasavi College of Engineering
B.E. in Computer Science and Engineering (AI & ML)
Hyderabad, Telangana
Experience
2026 - Present
People Tech Technology Private Limited
AI Developer Intern
Working on computer vision and edge-device AI use cases, with growing hands-on exposure to deployment and validation workflows.
- Working close to computer vision use cases for autonomous drone and embedded edge-device applications.
- Gaining real exposure to model deployment, runtime optimization, validation, and on-device inference behavior.
- Learning how board bring-up, backend mismatches, environment setup, and embedded Linux debugging affect actual AI delivery.
- Building a stronger understanding of what it takes to move from a trained model to a working edge AI pipeline.
Projects
AI-Based Species Detection using Autonomous Drone Systems
A company edge-AI project focused on using autonomous drone systems for animal species detection in real environments.
Python
ONNX
QAIRT
QNN
Rubik Pi
This project came through my company work and was built around the idea of using autonomous drones for animal species detection in the field. It taught me much more than model building by exposing me to the full deployment journey, from preparation and conversion to real-time validation on Rubik Pi hardware.
- Worked through PyTorch and ONNX to Qualcomm DLC conversion using QAIRT.
- Learned quantization flow, runtime setup, backend handling, and embedded debugging.
- Validated 27 to 32 FPS live inference and observed strong NPU-side performance gains.
Vision & Voice Controlled Desktop Assistant
A Python desktop assistant that combines voice, text, and vision for more natural system interaction.
Python
OpenAI API
OpenCV
YOLO
Streamlit
I built a Python-based desktop assistant that combines voice commands, text input, and vision-based interaction to handle everyday operations in a more natural way.
- Enabled desktop tasks through voice commands and text-based interaction.
- Supported actions like opening applications, detecting objects, and assisting user workflows.
- Explored how multimodal input can improve usability over traditional command-only systems.
Object Detection using YOLOv8
A real-time object detection project built with YOLOv8 to identify and track visual objects efficiently.
Python
YOLOv8
OpenCV
Computer Vision
Deep Learning
I built this project to strengthen my understanding of real-time detection workflows using YOLOv8. It helped me explore model inference, visual processing, and how object detection can be applied in practical monitoring and recognition scenarios.
- Used YOLOv8 and OpenCV to detect and localize objects from visual input.
- Worked on inference flow, bounding box rendering, and result interpretation.
- Improved my hands-on understanding of applied computer vision pipelines.
AI-Powered Emergency Health Network
A scalable MERN platform built to improve hospital-patient coordination during emergencies.
React
Node.js
Express.js
MongoDB
Chatbot
I designed and developed a scalable MERN-based platform to improve coordination between hospitals and patients during emergency situations. The project focused on making communication faster, clearer, and more responsive when timing matters.
- Built the platform with MongoDB, Express.js, React, and Node.js.
- Integrated real-time alerting and chatbot-assisted interaction into the system.
- Improved response flow and usability for time-sensitive healthcare coordination.
Socket.io Chatroom
A basic realtime chat application built to understand live messaging, server events, and user communication flow.
Node.js
Express.js
Socket.IO
Moment.js
Foundation
This project helped me understand the basics of realtime web communication by building a chatroom application using sockets. It gave me practical exposure to message flow, server-client communication, and lightweight full-stack app structure.
- Built the chat application using Node.js, Express, and Socket.IO.
- Used Moment.js for time display and Foundation for the frontend layout.
- Learned how realtime events and connected user interactions work in practice.
Skills
Languages
Python, Java, C++, JavaScript, SQL
Computer Vision
OpenCV, YOLOv8, YOLOv11, YOLO-NAS, object detection, deep learning
Deployment
ONNX, QAIRT, QNN, DLC conversion, quantization, Qualcomm HTP or NPU inference
Frameworks and Tools
React, Node.js, Express.js, Docker, AWS, Linux, Git, Streamlit
Platforms
Rubik Pi, Raspberry Pi, NVIDIA Jetson, Qualcomm-based edge boards
Core Strength
Performance optimization, deployment workflows, debugging, and real-time inference validation
Achievements
TCS CodeVita
Secured All India Rank 1 in TCS CodeVita Season 13 Round 1, reflecting strong problem-solving and algorithmic performance at a national level.
NPTEL
Completed the 12-week Joy of Computing Using Python course and scored 85 percent, strengthening problem-solving through Python.
Coursera
Completed courses in Crash Course on Python and Foundations of AI and Machine Learning during 2025.
Cisco
Earned certifications in C++ Fundamentals, ITN, and Python Essentials, adding breadth to my programming foundation.