Backend Engineer building scalable systems and integrating AI/ML models into production-grade applications, with a focus on security.

🎓 B.Tech Computer Science (Information Security) — VIT Vellore, graduating May 2026.

• Backend-focused developer with strong applied machine learning experience
• Building real-world systems combining APIs, automation, and AI
• Interested in scalable architectures, backend performance, and secure system design

• Scalable REST APIs using FastAPI, Flask, Node.js, and Spring Boot
• AI-powered systems (LLMs, automation pipelines, ML models)
• Secure backend systems with authentication, authorization, and data protection
• Cloud-native applications using AWS and Google Cloud

• 3-model deep learning ensemble (EfficientNetV2-S, MobileNetV3, ConvNeXt-Tiny) for tumor classification
• EfficientNetB4 Attention U-Net segmentation with Dice ~0.88
• Novel Dynamic Risk Index (DRI) with closed-loop lesion-aware fusion
• Grad-CAM explainability + Groq Llama-4 radiology report generation + PDF export
• Focus: applied ML + system design + AI pipelines

• Converts call transcripts → structured voice-agent configs (JSON)
• Extracts intents, entities, and conversation flows automatically
• Designed versioned system (v1/v2) with changelog generation
• Focus: AI pipelines + automation + system design

• Converted binaries → grayscale images for pattern extraction
• Trained EfficientNetV2 on 15K+ samples across 31 malware families
• Achieved ~95% accuracy with strong macro F1-score
• Focus: applied ML + data preprocessing + model optimization

• LLM-powered API that generates structured task breakdowns from high-level goals
• Produces dependencies, subtasks, and timelines automatically
• Focus: LLM integration + backend API design

• Studied real-world vulnerabilities including RCE, IDOR, and large-scale data breaches
• Analyzed system failures and attack patterns in modern backend architectures
• Understanding of secure design principles and common backend security risks

• Prefer simple, scalable architectures over unnecessary complexity
• Focus on production-ready systems, not tutorial-level projects
• Design systems with security, maintainability, and performance in mind

FastAPI, Flask, Node.js, Express, Spring Boot

TensorFlow, PyTorch, Scikit-learn, OpenCV, Pandas, NumPy

PostgreSQL, MySQL, MongoDB

AWS, Google Cloud Platform, Git

React.js, HTML, CSS, HTMX

Python, Java, JavaScript, C++

n8n

• Artificial Intelligence — SmartBridge (Google for Developers)
• AI Fluency: Framework & Foundations — Anthropic
• Introduction to Model Context Protocol & Advanced Model Context Protocol — Anthropic

📧 tharunsridhar@gmail.com 🔗 LinkedIn 🔗 HackerRank 🔗 Hugging Face