Java Backend Developer | Spring Boot & Microservices |
Conceptual architecture of an enterprise public-safety backend platform built around secure, scalable, event-driven microservices.
The architecture illustrates the flow from client applications and devices through the API and security layers into Java/Spring Boot microservices, with Keycloak handling authentication, authorization, and token-based security.
Core backend services include Client Activation, OIDC XCAP, Core Management, CAT, Subscriber Management, Communication, Notification, and Integration services, with RabbitMQ supporting asynchronous event-driven communication and XDM providing centralized data-management capabilities.
The platform also integrates notification channels and external systems while supporting reliable backend responses, integrations, data synchronization, and distributed service workflows.
Technologies: Java, Spring Boot, Microservices, REST APIs, Keycloak, RabbitMQ, XDM, Docker, Git, SQL and distributed backend systems.
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BDO – Digital Banking Platform | Send Money & Bills Payment
Contributed to the design and development of core banking services for a digital banking platform, with a focus on Send Money and Bills Payment capabilities.
Designed and developed Java/Spring Boot microservices supporting end-to-end transaction workflows. The Send Money and Bills Payment solutions followed a receiver–consumer service model, providing clear separation between request handling, validation, transaction processing, and asynchronous processing.
The Receiver services handled request validation, business validations, data validations, beneficiary/biller validations, and other eligibility and business-rule checks before requests were passed for processing.
The Consumer services handled the actual transaction execution using Spring Boot transactional processing, maintaining transaction integrity and supporting reliable status and error handling.
Worked with Apache Kafka and AWS SQS for asynchronous messaging, event processing, and reliable communication between backend components. The services used MySQL for transactional and application data and were deployed on AWS infrastructure.
My responsibilities included backend service design, Java/Spring Boot development, microservices implementation, business and data validation, transaction processing, database integration, messaging workflows, debugging, testing, and production-oriented enhancements.
Technology: Java • Spring Boot • Microservices • Apache Kafka • AWS SQS • MySQL • AWS
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Standard Chartered – Unified Digital Onboarding Platform | Hong Kong & Mainland China
Contributed to the backend development of a digital onboarding platform supporting customer journeys across Hong Kong and Mainland China.
The platform was developed using Java, Spring Boot, Microservices, GraphQL, Azure, MySQL and Oracle, providing backend services for customer onboarding, application processing, account and product workflows, document management, verification, application tracking, and enterprise integrations.
Worked extensively with GraphQL APIs and Java/Spring Boot microservices, developing backend functionality, business logic, service integrations, and application enhancements within a distributed architecture.
Contributed to improving code reusability, maintainability, modularity, and separation of responsibilities through object-oriented design patterns and microservices-based development.
The platform connected customer-facing onboarding applications with backend services and enterprise systems, supporting secure and scalable digital account-opening and onboarding workflows across the supported regions.
Technology: Java 17 • Spring Boot • GraphQL • Microservices • Azure • MySQL • Oracle • REST APIs
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DummyLabs – E-Commerce Platform | Java, Spring Boot & Microservices
Designed and developed a modular e-commerce platform using Java, Spring Boot, Microservices, and MySQL, covering core customer and administrative workflows.
The platform included functionality for product management, customer registration and login, product browsing, cart management, order processing, inventory management, and checkout workflows.
A major focus of the development was the Admin Portal, providing secure administrative access and capabilities for managing products, users, orders, inventory, and platform data.
Developed backend microservices with clear separation of responsibilities, implemented REST APIs, integrated database operations with MySQL, and worked on business logic, data validation, and service-level integrations.
The project also involved improving existing backend code for readability, maintainability, and modularity, along with database optimization through SQL queries and stored procedures and ORM-based persistence using Hibernate.
Technology: Java • Spring Boot • Microservices • MySQL • Hibernate • REST APIs • AWS
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57
Backend, Cloud & DevOps
Fractional CTO | Software Architect | SaaS | AI Agents
Fractional CTO | Software Architect | SaaS | AI Agents
90M registered users · 17 countries · 99.99% uptime · $451M disbursed via DBT
Led Everwell Hub from a monolith to a horizontally scalable microservices platform (Java/Python, Docker/Kubernetes, GCP, CI/CD, IaC). As chief architect I personally designed the service decomposition; as CTO I led ~50 engineers plus system-integrator partners and the WHO/Ministry of Health partnership.
Deployed across 17 countries and scaled India's national TB platform (Ni-kshay) to ~90M registered patients and staff, with 6 lakh+ direct users, at 99.99% uptime, sub-second P95 latency, and 2h RTO / 4h RPO disaster recovery. The payments module disbursed $451M through direct benefit transfers.
Released the platform as MIT open source with full wiki, runbooks, and API contracts — adopted by partner health systems since.
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10x throughput · Kafka · 13 social channels · built for viral-scale message bursts
Redesigned an enterprise social platform's data layer into an event-driven architecture on Apache Kafka, ingesting and analysing real-time social data from 13 channels.
Sized for viral-scale bursts of 250–1,000 messages/minute per customer, with hybrid delivery paths: streaming (~15–20s average latency) and polling (~2 min), with a worst-case 5-minute latency ceiling.
Also set architecture standards across application, data, and AI, and rebuilt the React/TypeScript frontend for multilingual workspaces with automated WCAG 2.2 / ADA Section 508 accessibility checks. End-to-end throughput improved 10x. Confidential client engagement.
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25
Cold start: 17 min → under 3 min. Shard loading: 16 min → 50 sec. Serving cost: ~1/6 of a managed SageMaker equivalent.
Performance-engineered a LLaVA vision-language deployment on AWS as part of a specialised strike team, with the full write-up published on LinkedIn (linked under Completed Work).
Reduced end-to-end cold start from ~17 minutes to under 3 (66%) via custom Deep Learning AMIs (−32% immediately), ephemeral-storage model caching with parallel aria2c downloads (checkpoint loading 16 min → 50 sec), and 4-bit NF4 quantization (shard loading 41s → 18s). Along the way, diagnosed a storage-path bug where checkpoint shards kept re-downloading despite being baked into the AMI.
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Cut shipping time 40% with a production multi-agent system built around governance, not demos. Confidential client engagement.
Designed and deployed a multi-agent system covering the full product lifecycle: PRD generation → system design → multi-repo execution → code review → compliance/security checks → release management. Shipping velocity improved 40% across cross-functional teams.
Built on a Backstage-based engineering knowledge base with MCP connectors to JIRA, GitHub, Slack, and Teams; LLM workloads on OpenAI and Anthropic via A2A-style agent orchestration, implemented in Python/Bash tooling.
Shipped with an enterprise AI governance layer: hallucination detection with knowledge-base remediation, model drift monitoring, and audit logging for regulated environments.
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51
Java Engineer Building AI Automations & Integrations
AI Business Operations Automation Suite
Built a centralized AI-powered automation platform designed to streamline internal business operations and eliminate repetitive manual work.
The system automates workflows across departments including operations, finance, customer support, reporting, and administration. By integrating AI, APIs, databases, and business tools, the platform significantly reduces operational overhead while improving efficiency and accuracy.
Key Features:
• Automated invoice processing
• AI-powered document analysis
• Internal workflow automation
• Approval and task management system
• Automated report generation
• Email and notification automation
• CRM and database integration
• Real-time analytics dashboard
• Role-based access control
• Custom business process automation
Business Impact:
✓ Reduced manual operational workload
✓ Faster processing of business tasks
✓ Improved team productivity
✓ Reduced human errors
✓ Centralized operational visibility
✓ Scalable automation infrastructure
Technology Stack:
• Java
• Spring Boot
• PostgreSQL
• OpenAI API
• REST APIs
• Workflow Automation
• Dashboard Analytics
This solution demonstrates how AI and automation can transform traditional business operations into efficient, scalable, and data-driven workflows.
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Built a collection of AI-powered business automation systems focused on solving real operational problems.
Projects included:
🚀 AI Customer Support Assistant
An OpenAI-powered support system that answers customer questions using company documentation, FAQs, and knowledge bases while reducing repetitive support workload.
🚀 AI Lead Enrichment & CRM Automation
Automated lead qualification, enrichment, CRM synchronization, and outreach workflows to improve sales efficiency and conversion rates.
🚀 AI Business Operations Automation Suite
Centralized automation platform for internal operations, reporting, document processing, notifications, approvals, and workflow management.
🚀 AI Document Processing & Workflow Automation
AI-powered document extraction, classification, validation, and routing system designed to eliminate manual data entry and processing bottlenecks.
Tools & Technologies:
• OpenAI
• AI Automation
• Workflow Automation
• Java
• Spring Boot
• PostgreSQL
• REST APIs
• CRM Integrations
My goal is to build AI systems that automate repetitive business processes, reduce operational costs, and help teams scale more efficiently.
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AI Document Intelligence & Invoice Processing Platform
Description
Built an AI-powered document intelligence platform designed to automate invoice processing, document classification, data extraction, and approval workflows.
The system uses AI, OCR, and workflow automation to process business documents automatically, eliminating manual data entry and reducing processing delays across finance and operations teams.
By integrating with existing databases and business systems, the platform transforms unstructured documents into actionable business data.
Key Features
• AI-powered document analysis
• Invoice data extraction
• OCR-based document processing
• Automated approval workflows
• Purchase order matching
• Vendor management integration
• Document classification
• Data validation system
• Real-time processing dashboard
• Automated reporting and audit trails
Business Impact
✓ Reduced manual data entry
✓ Faster invoice processing
✓ Reduced processing errors
✓ Improved financial visibility
✓ Faster approval cycles
✓ Reduced operational costs
✓ Increased productivity across teams
Technology Stack
• Java
• Spring Boot
• PostgreSQL
• OpenAI API
• OCR Processing
• REST APIs
• Workflow Automation
• Analytics Dashboard
Results
• Automated document processing workflows
• Reduced invoice processing time by approximately 75%
• Improved data accuracy
• Reduced manual administrative effort
• Accelerated approval and payment cycles
This solution demonstrates how AI-powered document intelligence can automate finance and operational processes while improving accuracy, compliance, and efficiency.
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AI Lead Enrichment & CRM Automation Platform
Built an AI-powered lead enrichment and CRM automation platform designed to help sales teams identify high-quality prospects, enrich lead data automatically, and streamline outreach workflows.
The platform combines AI, external data sources, CRM systems, and workflow automation to reduce manual research while improving lead quality and sales efficiency.
Key Features
• Automated lead enrichment
• Company and contact research
• AI-generated personalized outreach emails
• CRM synchronization
• Lead scoring system
• Sales pipeline automation
• Contact validation
• Automated follow-up workflows
• Multi-source data aggregation
• Analytics and performance tracking
Business Impact
✓ Reduced manual lead research
✓ Increased sales team productivity
✓ Improved lead quality
✓ Faster prospect qualification
✓ Higher outreach efficiency
✓ Better CRM data accuracy
✓ Increased pipeline visibility
Technology Stack
• Java
• Spring Boot
• PostgreSQL
• OpenAI API
• REST APIs
• CRM Integrations
• Workflow Automation
• Analytics Dashboard
Results
• Reduced lead research time by approximately 80%
• Automated prospect enrichment process
• Improved CRM data quality
• Increased sales team efficiency
• Faster lead qualification workflows
This solution demonstrates how AI can automate prospect research, improve lead quality, and accelerate sales operations.
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Full-stack dev | Clean modern UI | AI Powered | Deployment
Full-stack dev | Clean modern UI | AI Powered | Deployment
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Technical Blog Writer: DevOps, Cloud Computing, Big Data
Technical Blog Writer: DevOps, Cloud Computing, Big Data
Senior Developer, AI Integration Architect, Consultant
Full-stack developer turning messy ideas into fast, function
