🔭 I'm a Software Engineer with hands-on experience building backend services, cloud systems, and data-driven applications. I work mostly with Java, Python, Spring Boot, and AWS, and I enjoy solving practical engineering problems that improve performance, reliability, and scalability.

I've built and deployed microservices, integrated third-party APIs, optimized databases, and improved system throughput through clean engineering and testing practices. I've also explored machine learning through my MSc research on GAN-based synthetic data generation.

I like working in teams where code quality, clear communication, and ownership matter. I'm open to backend, cloud, and platform engineering roles where I can contribute and continue learning.

April 2023 - August 2024

• Engineered and deployed 10+ scalable microservices using Spring Boot and Java, enhancing application modularity, scalability, and ease of maintenance.
• Constructed and preserved backend services leveraging AWS (EC2, SES, SNS, SQS, CloudWatch), boosting system reliability by 10% and overall scalability.
• Integrated Meta and Instagram advertising APIs to streamline data processing and analytics, resulting in a 25% surge in data throughput.
• Optimized database performance by crafting and refining MySQL schemas, cutting query execution time and boosting data retrieval efficiency by 20%.
• Led backend code restructuring initiatives, yielding a 20% uplift in performance and a 25% diminishment in maintenance time after thorough code reviews.
• Collaborated cross-functionally with Front-End Teams to ensure seamless integration and smooth user experience, reducing user-reported issues.

February 2023 - March 2023

• Formulated and streamlined Spring Boot RESTful APIs, significantly reducing API response time by 20%.
• Developed comprehensive unit tests for core functionalities, improving code stability and reducing defect rates by 15%.
• Actively engaged in code review sessions, providing constructive feedback and adhering to best practices for high-quality code.

September 2024 - September 2025

• Pioneered DCGAN and CGAN models using Python, TensorFlow, and Keras to produce 256×256 synthetic images with near-original dataset quality.
• Architected novel approaches for dataset augmentation, resulting in a 15% increase in model training efficiency.
• Executed comprehensive evaluation metrics, demonstrating GAN model performance comparable to industry benchmarks.

MSc Computing (Artificial Intelligence) — Dublin City University, Ireland
September 2024 - September 2025

Master in Computer Applications (MCA) — Sinhgad Institute of Management, India
2020 - 2022 | First Class with Distinction (84.74%)

Bachelor in Computer Applications (BCA) — Marathwada Mitra Mandal's College of Commerce, India
2017 - 2020 | First Class (65%)

DSA & Memory Education Tool

Most Java memory tools — and most textbooks — quietly teach wrong mental models. I identified 8 specific misconceptions and built a visualizer that corrects each one through how it actually renders, not through a warning label in the corner.

• HashMap displays entries by hash bucket index — because HashMap does not preserve insertion order
• LinkedList spawns individual node cards scattered across the heap — because each node is a separate allocation, not one contiguous block
• Two-phase GC — objects become GC-eligible first, collected after a random delay — because GC is non-deterministic
• ArrayList renders as contiguous indexed cells, Stack as a LIFO tower — the visual matches the actual memory behavior

• Visualizes Stack, Heap & String Pool in real time with animated SVG reference arrows
• Supports 8+ data structures with live Add / Remove controls — no re-run required
• Simulates String interning, Integer cache (−128 to 127), and the == trap above 127
• Canvas-rendered BST with dynamic layout and in-order traversal display
• Zero dependencies — entire application ships as a single HTML file

Skills: Data Structures Java Memory Model Canvas API JavaScript HTML CSS

Links: Live Demo · Repository

SQL & Database Education Tool

Most people learn SQL by memorizing syntax. The deeper issue is the lack of a clear mental model of what actually happens inside the database engine when a query runs. I built a visualizer that animates every operation in execution order — rows being scanned, filtered, joined, grouped, sorted, and mutated — step by step, exactly as MySQL processes them.

• Schema canvas renders CREATE TABLE statements as draggable table cards with animated dashed foreign key arrows connecting child → parent columns in real time
• JOIN animator scans rows one-by-one with a glowing beam between tables — INNER, LEFT, RIGHT, and FULL JOIN produce visually distinct Venn diagrams and result streams
• WHERE engine supports LIKE, IN, BETWEEN, IS NULL, IS NOT NULL — matching rows flash green, filtered rows receive a strike-through animation before removal
• DML animations
  • INSERT → new rows appear with a green flash
  • UPDATE → matched rows highlight yellow with values applied live
  • DELETE → rows strike through before removal
  • TRUNCATE → instant wipe with side-by-side comparison explaining how it differs from DELETE
• GROUP BY + Aggregates (SUM, AVG, COUNT, MIN, MAX) animate as proportional bar charts per group, with NULL exclusion indicators
• Execution Order Explainer — side-by-side view of how SQL is written vs how MySQL actually executes it, with live step highlighting
• ORDER BY, LIMIT, OFFSET animate result slicing with a stats card showing total / filtered / returned row counts
• Zero dependencies — entire application ships as a single HTML file and runs in any modern browser

• Real-time animated query execution engine
• Interactive schema builder with foreign key relationship visualization
• Supports SELECT, JOIN, WHERE, GROUP BY, ORDER BY, LIMIT, INSERT, UPDATE, DELETE, TRUNCATE
• SVG-powered animations for row flow and relational links
• No backend required — fully client-side simulation

Skills: SQL MySQL Query Execution Engine Database Internals JavaScript HTML CSS SVG Animation

Links:
Live Demo
Repository

Production-Grade Gmail-to-Google Drive Automation

Designed and built a production-grade automation service that continuously scans Gmail for incoming emails with attachments and synchronizes them to Google Drive with zero duplication. The system handles real-world challenges like race conditions, concurrent execution, and persistent state management.

Key Features:

• Secure OAuth2-based integration with Gmail API and Google Drive API
• Message-level idempotency using MySQL persistence — guaranteed exactly-once processing
• Fault-tolerant pipeline with automatic background scheduling (3-minute intervals)
• Manual execution support through REST endpoints
• Human-readable file renaming system with cross-platform path safety
• Sender-based local organization for better file management
• Strict security practices using environment variables and secret management

Technical Highlights:

• Solved race condition handling and concurrent execution safety
• Engineered persistent state management for reliability across crashes and restarts
• Built production-style error handling and logging

Skills: Python FastAPI Gmail API Google Drive API MySQL OAuth2 APScheduler REST API SQL Git

Links: Repository

Scalable AWS-Powered Image Gallery

Designed and deployed a live cloud-native photography platform using AWS. The system automatically indexes newly uploaded images from Amazon S3 via event-driven Lambda functions and serves content globally using CloudFront CDN, implementing modern performance optimization and UX patterns.

Key Features:

• Event-driven architecture with AWS Lambda for automatic image indexing
• CloudFront CDN for global content distribution
• Infinite scrolling, lazy loading, and responsive grid layouts
• Interactive image viewer with keyboard navigation
• Telemetry tracking and performance monitoring
• Production-grade caching strategies and optimization

Skills: AWS (S3, Lambda, CloudFront) CDN Architecture Event-Driven Architecture HTML5 JavaScript Responsive Design Performance Optimization UI/UX Design

Links: Live Demo · Repository

Interactive Algorithm Demonstration

Interactive tool that brings sorting algorithms to life with step-by-step visualization of 6 different sorting techniques, helping learners understand algorithm behavior and performance characteristics.

Technologies: Python JavaScript DOM HTML CSS

Created: July 2022

• Data Structures And Algorithms by NPTEL, IIT Madras (April 2022)
• Algorithmic Toolbox by Coursera (UC San Diego and HSE University) (July 2021)
• JavaScript by Udemy (December 2021)