Software engineer with hands-on experience building backend systems and machine learning applications end-to-end from REST API design and database integration to model training, ensemble design, and LLM-powered pipelines. Built real-world systems using Python, Java, FastAPI, Flask, Spring Boot, PyTorch, TensorFlow, and Groq LLM API, with applied work in computer vision, transfer learning, GradCAM explainability, and automated report generation. Comfortable across the stack backend architecture, AI integration, and frontend wiring.

• I like building things that actually run APIs, pipelines, ML systems
• Comfortable going from model training to deployment and wiring it to a backend
• Curious about secure system design and how things break in production

• REST APIs with FastAPI, Flask, and Spring Boot
• Deep learning systems — training, ensembling, and serving models
• LLM integrations and automation pipelines
• Backend systems with clean structure and database design

PyTorch FastAPI Groq Llama-4 OpenCV GradCAM

• 4-model ensemble (EfficientNetV2-S, MobileNetV3, DenseNet-201, ConvNeXt-Tiny) for MRI classification
• EfficientNetB4 Attention U-Net segmentation : Dice ~0.88
• Dynamic Risk Index (DRI) with lesion-aware fusion for prediction reliability scoring
• GradCAM explainability + Groq Llama-4 for radiology report generation + PDF export
• FastAPI backend — endpoints for upload, inference, report generation, Google Drive storage

Python FastAPI n8n JSON

• Parses call transcripts and outputs structured voice-agent configs (JSON)
• Extracts intents, entities, slots, and dialog flows from raw text
• Versioned output system (v1/v2) with changelog generation

TensorFlow EfficientNetV2 Python NumPy

• Converted PE binaries to grayscale image tensors for visual pattern classification
• Fine-tuned EfficientNetV2 on 15K+ samples across 31 malware families
• ~95% test accuracy, macro F1 ~0.96

Java Spring Boot MySQL REST APIs

• RESTful CRUD API for employees and tasks, backed by MySQL
• Standard layered architecture — Controller, Service, Repository

• Studied real-world vulnerabilities : RCE, IDOR, data breaches
• Looking at how modern backend systems fail and how to design around it

Backend : FastAPI · Flask · Spring Boot

ML / AI : PyTorch · TensorFlow · Scikit-learn · OpenCV · Pandas · NumPy

LLMs : Groq Llama-4 · MCP (Model Context Protocol)

Databases : PostgreSQL · MySQL · MongoDB

Cloud : AWS · Google Cloud · Git · GitHub

Frontend : React.js · HTML · CSS

Languages : Python · Java · JavaScript · SQL

Automation : n8n

• Artificial Intelligence : SmartBridge (Google for Developers)
• AI Fluency: Framework & Foundations : Anthropic
• Introduction to MCP & Advanced MCP : Anthropic

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