Software systems
Microservices · Integrations
Problem:
Build a scalable platform for handling consultations, payments, and notifications with high reliability and clear operational flows. System Design: Designed a microservices-based architecture using Spring Boot, Kafka, PostgreSQL, and Angular, with secure authentication and asynchronous integrations.
Implementation:
Implemented OAuth2 + JWT authentication, event-driven messaging with Kafka, and third-party integrations for payments and messaging, with retries and idempotent processing where reliability mattered.
Impact:
Delivered production-ready booking and messaging workflows with scalable backend services, fault-tolerant processing, and maintainable integrations.
Spring Boot
Spring AI
OAuth2
JWT
PostgreSQL
Angular
Kafka
WhatsApp API
Payment API
AI product engineering
LLM integration · Auth
Problem:
Build an AI-assisted platform that integrates intelligent content generation into a secure, usable web product. System Design: Implemented backend services and product flows combining Spring Boot, Spring AI, Angular, authentication, and payment workflows.
Implementation:
Integrated LLM APIs through Spring AI, secured user sessions with JWT, connected MySQL persistence, and implemented monetized workflows with payment provider integration.
Impact:
Delivered AI-powered functionality inside a maintainable product architecture, with clear boundaries between model-powered features and core application services.
Spring Boot
Spring AI
JWT
MySQL
Angular
Payment API
LLM API integration
Software systems
Scheduling · Platform
Problem:
Build a reliable scheduling platform for learning workflows, instructor management, and day-to-day platform operations. System Design: Implemented backend services, integrations, and frontend components supporting scheduling and operational consistency.
Implementation:
Implemented service discovery with Eureka, API integrations for payments and calendar synchronization, and frontend/admin touchpoints required for daily operations.
Impact:
Delivered stable scheduling workflows and backend integrations designed for maintainability, operational continuity, and ease of extension.
Spring Boot
Angular
WordPress
Eureka
Payment API
Calendar API
AI / ML · Research
Edge ML · Distributed
Problem:
Build a distributed ML system that operates under real deployment constraints such as edge hardware, sensor ingestion, and limited resources. System Design: Built the full training and inference pipeline, including distributed coordination, storage, and data ingestion.
Implementation:
Implemented gRPC-based coordination between nodes, time-series storage in InfluxDB, PyTorch models for forecasting, and reliable ingestion from sensor APIs.
Impact:
Delivered an end-to-end ML engineering system covering data ingestion, preprocessing, training, evaluation, and inference under distributed edge constraints.
Python
gRPC
PyTorch
InfluxDB
Sensors API integration
LSTM
GRU
Data · AI / ML
DQ · Benchmarking
Problem:
Create a reliable data and experimentation pipeline for thermal modeling on noisy real-world building data. System Design: Implemented preprocessing, validation, anomaly handling, visualization, and model baselines to support repeatable experimentation.
Implementation:
Implemented automated data quality checks, anomaly removal, visualization, and ML baselines using XGBoost and PyTorch.
Impact:
Improved data reliability and made model comparisons reproducible through structured preprocessing and benchmark-oriented experimentation.
Python
PyTorch
XGBoost
LSTM
Data visualization
Quality checks
Anomaly removal