ALPHA TL Manager · NIE Mysore · AI/ML Engineer
CS grad specializing in AI/ML. Deployed 10+ production ML models, optimized data pipelines achieving 25% faster throughput, and led cross-functional teams of 10+ members.
An enterprise-grade web application ensuring complete file privacy using end-to-end hybrid cryptography (AES-256-GCM + RSA-4096-OAEP) and seamless Google Drive integration.
Solve data privacy concerns inherent in traditional cloud storage by implementing end-to-end encryption. Files must be encrypted before leaving the user's device and only be decipherable by the exact intended recipient.
Successfully developed a production-ready secure file-sharing platform that guarantees data confidentiality, integrity, and privacy while enabling practical cloud collaboration.
An intelligent, fully offline computer vision system detecting vehicles and pedestrians in real-time, while simultaneously estimating age and gender using YOLOv8m and MiVOLO V2.
Demonstrate practical implementation of state-of-the-art object detection and demographic analysis locally, ensuring zero cloud dependency and strict data privacy.
Built a comprehensive AI-powered application delivering high accuracy for smart city monitoring, retail analytics, and public safety with highly efficient local hardware utilization.
A fully offline, privacy-focused AI writing assistant integrating Mistral 7B, Phi-2, and FLAN-T5-base for academic report generation, autocomplete, and grammar correction.
Support researchers and professionals in creating high-quality academic documents using an intelligent writing environment that processes everything locally, eliminating internet dependence and ensuring absolute data privacy.
The system continuously learns from user corrections, building a personalized writing profile via Pickle serialization. It analyzes vocabulary, sentence structures, and formatting preferences to align perfectly with the user's individual writing style over time.
Developed a comprehensive offline AI research assistant capable of handling academic papers, executive summaries, and technical reports while actively adapting to user preferences with guaranteed data security.
A comprehensive machine learning platform to analyze network traffic, discover hidden attack patterns via FP-Growth, and classify threats using XGBoost and DBSCAN.
Go beyond traditional intrusion detection by not only detecting attacks but also uncovering hidden relationships among 85+ network traffic features to provide interpretable, actionable insights for cybersecurity analysts.
Successfully integrated pattern mining, classification, clustering, and anomaly detection into a unified platform. Achieved high accuracy and faster prediction speeds by reducing 85 features to the 10 most critical ones.
A low-cost, production-grade SCADA system for real-time industrial monitoring and remote device control — built entirely with off-the-shelf hardware and open protocols.
Replace expensive proprietary SCADA setups with a cost-effective alternative using commodity IoT hardware. The system needed to be offline-capable, remotely accessible, and maintainable by non-specialist staff.
ESP32 acts as the Modbus Slave, collecting DHT11 temperature/humidity readings into mapped Modbus registers. A Raspberry Pi 5 serves as the Modbus Master, polling the ESP32 every 2 seconds via Modbus RTU over serial (UART). The Pi also runs a lightweight local web server (Flask/Python) that exposes a live dashboard with real-time readings and device control buttons.
System runs continuously with 99% data accuracy across 72-hour stress tests. Remote access latency under 200ms via Cloudflare Tunnel. Total hardware cost under ₹4,000 vs. ₹50,000+ for commercial equivalents.
Modbus RTU/TCP, embedded C, Python (pymodbus, Flask), Cloudflare Tunnel, SQLite, IoT system architecture, real-time dashboard development.
An automated ML pipeline for malware detection using structured cyber threat intelligence in STIX format — combining unsupervised anomaly detection with supervised classification for layered defence.
Automate the process of ingesting raw threat feeds, normalizing them into the STIX 2.1 standard, and applying ML models to detect both known and unknown malware with minimal analyst intervention.
Hybrid two-layer approach improved overall detection reliability by 30% over a single-model baseline. False positive rate reduced by 18% compared to Isolation Forest alone. End-to-end pipeline processes a 10,000-indicator feed in under 45 seconds.
STIX 2.1 standard, Isolation Forest, SVM, feature engineering from threat intelligence data, end-to-end ML pipeline automation, Python (scikit-learn, stix2).
A predictive maintenance system for NVMe SSDs that uses SMART telemetry data and XGBoost — tuned via Genetic Algorithm — to forecast drive failures before they occur.
Provide data centre operators and end users with an early warning system for NVMe drive failure, reducing unplanned downtime and data loss. Target: predict failure with at least 90% accuracy with actionable lead time of 24–72 hours.
Processed 10,000 drive records in 137 seconds end-to-end. Achieved 92% failure prediction accuracy with 89% recall on the minority (failure) class. GA tuning added 4.2% accuracy improvement over default XGBoost hyperparameters.
XGBoost, Genetic Algorithms (DEAP), feature engineering, predictive maintenance, NVMe/SMART telemetry parsing, model evaluation (precision/recall/F1), Python.
A Retrieval-Augmented Generation (RAG) chatbot that answers user questions from uploaded documents using semantic search — not keyword matching — backed by LLaMA 2 and Chroma DB.
Enable users to query large documents (PDFs, reports, manuals) in natural language and receive accurate, grounded answers — eliminating hallucination by always anchoring responses to retrieved document chunks.
90% answer accuracy on a held-out evaluation set of 200 QA pairs. Retrieval precision@3 of 88%. Runs fully offline on consumer hardware (8GB RAM, no GPU required).
RAG architecture, vector databases (Chroma DB), LLM integration, LangChain, Sentence Transformers, GGUF quantization, Python.
A robust fraud detection pipeline for credit card transactions tackling the real-world challenge of extreme class imbalance (only 0.17% fraud cases in 284,807 transactions).
Build a production-ready fraud classifier that maintains high precision and recall simultaneously — minimising both missed fraud (false negatives, which cost money) and false alarms (false positives, which hurt customer trust).
Random Forest outperformed both baselines. Final model: 95% precision, 93% recall, F1-score 0.94 on held-out test set. ROC-AUC of 0.98. Pipeline throughput: 50,000 transactions processed per minute.
SMOTE, imbalanced learning, threshold optimization, Random Forest, XGBoost, feature engineering, precision/recall tradeoff analysis, Python (scikit-learn, imbalanced-learn).
An end-to-end smart agriculture system providing real-time soil health monitoring and AI-powered crop/fertilizer recommendations for small-scale farmers — a Govt. of Karnataka Grassroot Innovation 2025 finalist.
Eliminate the dependence on expensive and infrequent laboratory soil testing for small farmers. Provide continuous, affordable, and actionable soil intelligence directly in the field, reducing input costs and improving yield decisions.
Tested on 3 farms in the Mysore district over 8 weeks. Reduced manual soil testing time by 80% (from 2 hours per sample trip to 20 minutes per week of dashboard review). Crop recommendations matched expert agronomist advice in 85% of cases. Avg. sensor power draw: 12mA — projected battery life of 6 months on a 10,000mAh pack.
LoRa communication, ESP32 embedded programming, Modbus RS485, edge AI inference, Random Forest, agricultural domain knowledge, low-power IoT design, Raspberry Pi, Python.