M.Tech Data Science Student | Machine Learning Engineer | AI Research Enthusiast
I'm a passionate data scientist who transforms complex problems into intelligent solutions. My expertise spans the complete machine learning lifecycle—from data collection and model development to production deployment and monitoring. I specialize in building scalable, production-ready systems that deliver measurable business impact.
- Full-Stack Data Science: End-to-end ML pipeline development
- Production Focus: Scalable systems with robust MLOps practices
- Business-Driven: Solutions that create measurable value
- Research Oriented: Cutting-edge techniques applied to real problems
- Problem Solver: Creative approaches to complex challenges
Complete MLOps Implementation
- Advanced ML Pipeline with feature engineering and model optimization
- High-Performance API with fast response times and batch processing
- Business Intelligence with comprehensive analytics and ROI analysis
- Production Deployment using Docker and MLflow tracking
Technologies: Python XGBoost FastAPI Docker MLflow
Advanced Computer Vision Application
- Multi-Modal Architecture combining multiple data sources
- Deep Learning Models with convolutional neural networks
- Interactive Interface with real-time prediction capabilities
- Research Implementation with experiment tracking and validation
Technologies: PyTorch Computer Vision Multi-Modal AI Streamlit
Advanced AI Research Project
- Multi-Agent Systems with complex environment simulation
- Reinforcement Learning algorithms and optimization techniques
- Performance Monitoring with comprehensive evaluation metrics
- Research Innovation pushing boundaries of current methods
Technologies: PyTorch Reinforcement Learning Multi-Agent Systems Simulation
| Credential | Institution | Focus Area |
|---|---|---|
| IBM Data Science Professional | IBM | Complete Data Science Pipeline |
| Remote Sensing Image Acquisition, Analysis and Applications | UNSW | Geospatial Mapping, Computer Vision, Dimensionality Reduction |
| AWS Academy ML Foundation | Amazon Web Services | Cloud ML & Deployment |
| Image Processing Certification | MathWorks | Computer Vision & Image Analysis |
Machine Learning Engineering Data Analytics & Visualization
Deep Learning & Neural Networks MLOps & Cloud Deployment
Computer Vision Applications Natural Language Processing
Real-Time Systems & APIs Research & Innovation
| Achievement | Impact | Domain |
|---|---|---|
| High-Accuracy Models | Consistently Strong Performance | Multiple Domains |
| Production Systems | Fast & Scalable Deployment | MLOps & Engineering |
| Research Contributions | Innovation & Publications | AI Research |
| Open Source Projects | Community Impact | Software Development |
| Technical Leadership | Mentoring & Knowledge Sharing | Team Collaboration |
- Production ML Systems: Complete Guide
- Comprehensive technical implementation tutorials
- Best practices for ML in production
- Open Source: Multiple repositories with detailed documentation
- Technical Mentoring: Supporting fellow data scientists and students
- Industry Applications: Real-world problem solving and innovation
Enterprise ML Solutions Research & Development
Open Source Contributions Technical Education & Mentoring
Startup & Innovation Projects Consulting & Advisory Roles
Data Science Internships | ML Engineering Roles | Research Collaborations | Open Source Projects
"Passionate about turning data into actionable insights and building AI systems that make a difference"