Building scalable backend systems, data-driven applications, and machine learning solutions.
• Software Engineer with 1 year experience in backend development and system integration
• Skilled in Java, Spring Boot, Python, and distributed systems
• Strong interest in building scalable backend platforms and data analytics solutions
• Experience developing microservices, REST APIs, and real-time monitoring systems
• Passionate about solving real-world problems using data, cloud, and machine learning
• Designing and building scalable backend applications using Java and Spring Boot
• Developing distributed systems and microservices-based architectures
• Working with event-driven systems and real-time data processing using Kafka
• Building data-driven applications and analytics solutions
Scalable backend platform for generating short URLs with high performance.
Tech Stack: Java, Spring Boot, Redis, PostgreSQL, Docker
Highlights
- Supports 50K+ shortened URLs
- Handles 1K+ concurrent redirects/sec
- Redis caching for sub-10ms latency
- Built-in rate limiting and analytics tracking
Real-time monitoring system that processes operational metrics and predicts system failures.
Tech Stack: Java, Spring Boot, Apache Kafka, PostgreSQL
Highlights
- Streams 1K+ system metrics per minute
- Performs real-time anomaly detection
- Secure JWT-based APIs
- Enables root cause analysis
Deep learning system that detects human fall events from video input.
Tech Stack: Python, Flask, Deep Learning
Highlights
- Fine-tuned TimeSformer, ConvNeXT, ResNet
- Achieved 93.5% classification accuracy
- Integrated web interface for predictions
Analytics project analyzing insurance data to identify pricing patterns and customer trends.
Tech Stack: Python, Pandas, Matplotlib
Highlights
- Conducted exploratory data analysis on insurance datasets
- Built visual insights for premium distribution patterns
- Generated insights supporting pricing evaluation
Interactive Power BI dashboard analyzing enterprise sales and operational performance.
Tech Stack: Power BI, DAX, Data Analytics
Highlights
- Identified revenue drivers and operational bottlenecks
- Built drill-down interactive dashboards
- Improved business visibility across regions
Programming Languages: Java, Python, R
Backend Development: Spring Boot, Flask, Django, REST APIs, Microservices
Databases: MySQL, PostgreSQL, MariaDB, Redis
Data & Analytics: Power BI, Pandas, Matplotlib, Data Visualization
Tools & Technologies: Docker, Git, Jenkins, Kafka
Core CS: Data Structures, Algorithms, Operating Systems, DBMS, Computer Networks
⭐ Open to opportunities in Software Engineering, Backend Development, and Data Engineering