Hi, I’m Chirag. I like exploring the space where systems and machine learning meet, making big things scale and clever things work smoothly. I’ve built tools for distributed training, cloud infrastructure, and research in computer vision. I enjoy learning, building, and sharing along the way. If you ever want to swap ideas or just chat tech, feel free to reach out.

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• Key Skills
• Education
• Experience
• Certifications
• Featured Projects

Master of Computer Science — North Carolina State University · GPA: 4.0/4.0
Bachelor of Information Technology — SSN College of Engineering, Anna University · GPA: 9.16/10

FinSurge — SWE Intern
Built an OCR pipeline (Flask API + React UI), containerized with Docker and deployed on AWS. Automated pipelines with CI/CD.

AWS Certified Cloud Practitioner

Repo: unit-test-generation
Highlights: Automated test-suite generator using CodeBERT/CodeT5/CodeLlama; retrieval-augmented context with Pinecone/LlamaIndex; RLHF pipeline.
Stack: Python · PyTorch · RAG · Pinecone · LlamaIndex · GitHub Actions

Repo: LPRNet_CSC591
Highlights: ~78% compression via Post Training Quantization + 2:4 pruning + JIT; 2.2× faster inference on ONNX Runtime with accuracy preserved.
Stack: ONNX Runtime · PyTorch · TVM · Quantization · Pruning

Repo: Distributed-Threat-Detection-System
Highlights: Scalable, fault-tolerant real-time security monitoring across k8s nodes; Kafka-based event streaming; automated quarantine workflow.
Stack: Go · Kafka · Kubernetes · Docker · gRPC · CI/CD

Repo: P2p_Network_Implementation
Highlights: Developed a distributed marketplace with buyers and sellers communicating over TCP through sockets and dockerized over a network Performed rigourous analysis to understand how these systems work.
Stack: C++ · Docker Networking · Thread Pools · Socket Programming

Repo: Integrating-Headless-Gazebo-into-GitHub-Actions
Highlights: Headless Gazebo simulations in CI; custom containers; multi-stage workflows with deterministic tests.
Stack: GitHub Actions · Docker · ROS · Gazebo

Repo: PigTracking
Highlights: YOLOv8 + ByteTrack + EfficientNet to classify feeding/lying/moving; evaluation tooling for time-series video.
Stack: PyTorch · YOLOv8 · ByteTrack · EfficientNet

Repo: MTS Highlights: Ruby + Ruby on Rails + MySQL; Docker/Kubernetes orchestration; comprehensive test coverage.
Stack: Ruby on Rails· MySQL · Docker · Kubernetes

_{© 2025 Chirag Bheemaiah PK}