About
Highly motivated and results-oriented B.Tech student specializing in Artificial Intelligence and Machine Learning, with a strong foundation in deep learning architectures, natural language processing, and digital twin technology. Proven ability to develop and deploy scalable AI/ML solutions, evidenced by contributions to predictive fault detection systems, high-fidelity digital twins with 95%+ accuracy, and advanced RAG chatbots. Seeking to leverage expertise in MLOps, GenAI, and data-driven innovation to drive impactful projects in a forward-thinking tech environment.
Work
Remote
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Summary
Modernized data monitoring infrastructure and engineered advanced AI/ML systems for predictive fault detection and anomaly detection, significantly enhancing system observability and operational reliability.
Highlights
Modernized data monitoring by migrating from a cumbersome custom FastAPI dashboard to a scalable Grafana and InfluxDB stack, decoupling analytics from the production database, dramatically improving system observability.
Engineered a predictive fault detection system using physics-based synthetic data generation, robust fault labeling, and custom LSTM model training, enabling reliable and scalable fault forecasting.
Built an unsupervised operational anomaly detection pipeline with LSTM autoencoder, latent features, and clustering for proactive issue identification.
Configured an alarm system using webhooks to deliver immediate updates on system alerts and operational faults, enhancing response times and system integrity.
Remote
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Summary
Developed high-fidelity digital twin solutions for solar inverters, achieving over 95% real-time performance accuracy, and pioneered a RUL estimation model to advance predictive maintenance.
Highlights
Developed a high-fidelity digital twin for solar inverters, delivering over 95% real-time performance accuracy.
Pioneered a RUL estimation model achieving 70% accuracy without failure data, significantly advancing predictive maintenance capabilities.
Built FastAPI backend services to seamlessly stream digital twin and RUL estimation analytics on dashboards, enhancing data accessibility.
Delhi, Delhi, India
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Summary
Conducted research and fine-tuned a Detection Transformer with ResNet50 on the WiderFace dataset, significantly improving accuracy on tiny face detection.
Highlights
Selected and fine-tuned a Detection Transformer with ResNet50 on the WiderFace dataset (Hard set), improving accuracy on tiny face detection.
Improved DETR's detection capabilities via object query augmentation, tuning, data augmentation, and backbone optimization.
Education
Skills
Machine Learning
Supervised Learning, Unsupervised Learning, Boosting, Random Forest.
Deep Learning
RNN, LSTM, Transformers, CNN architecture (ResNet-50, InceptionNet, VGG-16, RCNN, YOLO).
LLM & GenAI
LangChain, LangGraph, RAG, Multi-Agent Systems.
Programming & CS Fundamentals
Python, C++, OOPs, DSA, DBMS, SQL.
Libraries & Frameworks
PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, FastAPI, Streamlit, Dockers.