B.Tech Engineer in Artificial Intelligence & Data Science with a strong foundation in developing and deploying production-ready ML systems. I specialize in translating complex data challenges into robust, scalable software solutions, leveraging MLOps principles, advanced NLP techniques, and full-stack engineering discipline. Currently seeking opportunities to contribute to cutting-edge AI/ML projects in predictive analytics, conversational AI, and data engineering within Pune's thriving tech ecosystem.

• Predictive Modeling Excellence: Engineered critical infrastructure predictive maintenance solution achieving 85% Pointwise Classification Accuracy and F1-Score of 0.82 on test data (Pump Failure Detection System)
• Large-Scale NLP Systems: Built sentiment analysis models processing 100K+ data points with Macro-Average F1-Score of 0.78 for multi-class classification
• MLOps Production Readiness: Designed model architectures with automated retraining pipelines, CI/CD integration, and data drift monitoring for production deployment

• Student Community Leadership: Mentored 30+ developers across 4 project groups, delivered 8 technical workshops (Git, Android, ML fundamentals) driving practical skill application
• Industry 4.0 Prototyping: Cross-functional expertise bridging software and physical systems through advanced 3D printing (FDM/PolyJet) and CAD design
• Full-Stack Engineering Maturity: Robust foundation in TypeScript, Angular, NodeJS ensuring end-to-end solution delivery capability

🎤 Performing Arts:
Passionate singer and dancer with active participation in inter-collegiate competitions. These experiences have sharpened my confidence, creative thinking, and ability to perform under pressure—transferable skills essential for technical presentations and stakeholder communication.

I am committed to pushing technical boundaries and believe in the power of collaboration for solving complex AI challenges. I actively seek mentorship and partnership opportunities that drive innovation.

1. Advanced MLOps & Production AI

• Implementing robust CI/CD pipelines for ML models using GitHub Actions
• Achieving 100% deployment automation with comprehensive monitoring
• Model performance tracking and automated retraining strategies
• Kubernetes and container orchestration for scalable ML systems

2. Generative AI & LLM Applications

• Large Language Models (LLMs) for domain-specific applications
• RAG (Retrieval-Augmented Generation) architecture implementation
• Fine-tuning transformer models for specialized business use cases
• Prompt engineering and LLM optimization strategies

3. Computer Vision & Edge AI

• Real-time object detection and tracking systems
• Edge deployment of ML models for IoT applications
• Custom hardware-software integration for CV projects
• Contributing to open-source CV projects (tagged 'good first issue')

4. Data Engineering at Scale

• Building robust ETL pipelines for large-scale data processing
• Real-time data streaming and processing architectures
• Data quality monitoring and governance frameworks