Portfolio

Event-Driven E-Commerce Platform

Challenge: Legacy monolith handling 10M+ daily transactions needed complete architectural overhaul for Black Friday scale.

Solution: Decomposed into 15 microservices with event sourcing, CQRS, and Kafka event streaming.

Microservices architecture with event sourcing, API gateway, and distributed data stores

Technical Implementation:

• Event Store: Custom event sourcing with PostgreSQL and Redis
• API Gateway: Kong with rate limiting and circuit breakers
• Message Bus: Apache Kafka with exactly-once delivery
• Database Strategy: CQRS with read/write separation
• Monitoring: Distributed tracing with Jaeger and Prometheus

Results:

• 99.99% uptime during Black Friday weekend
• 3x throughput increase (30M requests/day)
• 40% reduction in P95 response time
• Zero data loss with event replay capabilities

AI-Powered Content Moderation System

Challenge: Social platform needed real-time content moderation for 1M+ posts daily with multilingual support.

Solution: Built hybrid AI system combining LLMs, computer vision, and traditional ML with human-in-the-loop feedback.

Multi-modal AI pipeline with LangChain agents, vector similarity, and human feedback loops

Technical Implementation:

• LLM Integration: OpenAI GPT-4 with custom fine-tuning
• Vector Database: Pinecone for semantic similarity matching
• Agent Framework: LangChain with custom tools and workflows
• Computer Vision: Custom CNN for image/video analysis
• Feedback Loop: Active learning with human moderator input
• Infrastructure: Kubernetes on AWS with GPU acceleration

Results:

• 94% accuracy in content classification
• Sub-100ms latency for real-time processing
• 60% reduction in false positives
• 5x faster moderation compared to human-only approach

Edge Computing IoT Platform

Challenge: Manufacturing client needed real-time analytics on 50,000+ IoT sensors with millisecond latency requirements.

Solution: Deployed edge computing infrastructure with federated learning and local AI inference.

Edge computing nodes with local AI inference, federated learning, and cloud synchronization

Technical Implementation:

• Edge Nodes: Kubernetes on ARM processors with local storage
• AI Inference: TensorFlow Lite models optimized for edge deployment
• Data Pipeline: Apache Kafka streaming with local buffering
• Federated Learning: Custom framework for distributed model training
• Cloud Sync: Intelligent data aggregation and model distribution
• Security: End-to-end encryption with hardware security modules

Results:

• <5ms latency for critical safety alerts
• 99.9% uptime across all edge locations
• 70% bandwidth reduction through edge processing
• Real-time anomaly detection preventing 12 critical failures