I architect and build scalable FinTech and enterprise SaaS solutions with modern technologies. With expertise in serverless microservices, cloud architecture, and team leadership, I deliver robust systems that drive business growth.
Quantifiable achievements and experience metrics that demonstrate expertise
Software Engineering & Leadership
Multi-tenant SaaS Platforms
Successfully Resolved
Top Performer Recognitions
Platforms Served
Mastered & Implemented
International Projects
Comprehensive technical stack and architectural patterns for building scalable solutions
Expertise in modern development frameworks and languages
Deploying and managing scalable cloud infrastructures
Designing and optimizing database architectures
Implementing scalable software architectures
Ensuring quality and reliability of software
Leveraging AI to optimize workflows and processes
Recognition for exceptional performance and competitive accomplishments
Recognized as Top Performer in both 2024 & 2025 for exceptional technical expertise and delivery impact across multiple projects.
Consecutive Year AwardsSuccessfully managed PokitPal platform serving ~3M users and designed Round-Up, a new SaaS FinTech product projected to reach 250K users.
Scaled to 3M+ UsersBuilt PAI (PokitPal AI) chatbot using AWS Bedrock, reducing support effort by ~2 hours per agent daily while maintaining high customer satisfaction.
Saved 2+ Hours/DayManaged and resolved 2,500+ production support tickets across multiple projects, significantly improving system reliability and client satisfaction.
2500+ Tickets ResolvedReal-time data processing application using Apache Kafka
IoT-Based Electrical Power Saving Module
Creative design of student board depicting innovative concepts
Enterprise-grade solutions with measurable business outcomes
Multi-tenant SaaS platform with automated round-up transactions, serverless microservices architecture, and real-time analytics dashboard.
Enterprise-scale loyalty SaaS serving multiple merchants with real-time point tracking, personalized offers, and AI-driven recommendations.
Comprehensive energy management SaaS with automated reporting, real-time dashboards, and predictive analytics for enterprise clients.
Innovative side projects showcasing technical expertise and passion for solving real-world problems
AI-powered code review tool that analyzes pull requests, suggests improvements, and detects security vulnerabilities using machine learning models trained on thousands of code repositories.
Multi-tenant analytics dashboard with real-time data visualization, automated reporting, and collaborative features for teams to monitor and analyze business metrics.
Secure, transparent voting system built on Ethereum blockchain ensuring voter anonymity, immutability of votes, and verifiable election results through smart contracts.
Technical articles and insights on software architecture, cloud computing, and SaaS development
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Feedback from colleagues, managers, and clients I've worked with
Progressive career path showcasing growth from Associate to Tech Lead roles
Tech Stack: Node.js, Express, React.js, MongoDB, AWS, Vercel, Firebase, Serverless, CI/CD, K6
Tech Stack: .NET, EF Core, Blazor, K6
Tech Stack: Node.js, React.js, Serverless, AWS, Keycloak, Stripe
Tech Stack: Java, JEE, Smart GWT, Spring, Struts2 MVC, Node.js, Express.js, React.js, Kafka
Let's connect and discuss how we can work together on your next project
Colombo, Sri Lanka
+94 77 123 4567
Traditional banking lacked automated micro-saving solutions. Users needed a seamless way to save small amounts daily without manual effort. Financial institutions also struggled to engage younger demographics with traditional savings products.
Built a multi-tenant SaaS platform with automated round-up transactions, serverless microservices architecture, and real-time analytics dashboard. Implemented secure transaction processing, customizable savings goals, and social sharing features to increase user engagement.
Loyalty programs were fragmented across multiple merchants with no unified platform for cross-merchant rewards and analytics. Customers carried multiple loyalty cards while merchants lacked insights into customer behavior across their partner network.
Developed enterprise-scale loyalty SaaS serving multiple merchants with real-time point tracking, personalized offers, and AI-driven recommendations. Built a unified rewards ecosystem allowing customers to earn and redeem points across partner merchants.
Enterprises struggled with manual energy tracking using spreadsheets and lacked real-time insights into carbon emissions and sustainability metrics. Compliance reporting was time-consuming and error-prone, making it difficult to meet environmental regulations.
Created comprehensive energy management SaaS with automated reporting, real-time dashboards, and predictive analytics for 50+ enterprise clients. Integrated with IoT sensors and utility APIs to provide accurate, automated data collection and carbon footprint calculations.
An AI-powered tool that analyzes pull requests, detects code issues, and suggests improvements to enhance development workflows.
Key interfaces and features of the CodeSense AI platform
Project overview, goals, and key achievements
CodeSense AI is an intelligent code review assistant that leverages machine learning to analyze pull requests, detect code smells, suggest improvements, and identify security vulnerabilities. Built as a personal project to explore AI/ML applications in software development workflows.
Challenges and pain points addressed by this project
Developers spend 20-30% of their time on code reviews, often finding similar issues repeatedly across different pull requests.
Manual reviews miss 30-40% of security issues, leaving applications vulnerable to potential attacks and exploits.
Review quality varies significantly between team members, leading to inconsistent code standards and quality across the codebase.
Architecture, technologies, and development approach
Client → API Gateway → Authentication → Rate Limiting
↓
Code Analysis Service → ML Model Inference
↓
Result Aggregation → Database (PostgreSQL)
↓
Notification Service → WebSocket/Email
↓
Dashboard (React) ← Caching (Redis)
Quantifiable achievements and performance metrics
Methodology, timeline, and key milestones
Duration: 2 weeks
Market research, technology evaluation, and requirement gathering. Analyzed 50+ open-source projects to identify common code patterns and issues.
Duration: 3 weeks
System design, technology stack selection, and prototype development. Created detailed architecture documentation and API specifications.
Duration: 8 weeks
ML model training, backend API development, and frontend implementation. Conducted daily code reviews and pair programming sessions with the team.
Duration: 2 weeks
Comprehensive testing, CI/CD setup, and production deployment. Conducted load testing with 10,000+ concurrent users and multiple stress scenarios.
Technical obstacles and how they were addressed
Challenge: Initial ML inference took 3-4 seconds per file, too slow for real-time feedback during development.
Solution: Implemented model quantization, async processing with Redis Queue, and pre-computed analysis for common code patterns, reducing latency to 2.1s average.
Challenge: Processing proprietary source code while maintaining privacy and security for enterprise clients.
Solution: Implemented end-to-end encryption, local processing capabilities, and an on-premise deployment option that keeps all code within client infrastructure.
Key learnings and planned enhancements
Integrate GPT-4 for natural language code explanations and contextual suggestions based on project history and team conventions.
Extend support to 10+ programming languages beyond Python and JavaScript, including Go, Rust, Java, and C++.
Add SSO, team management, advanced reporting, and integration with additional CI/CD platforms and issue trackers.