Technical Overview
AI System Architecture
I design and deploy full-stack AI systems from intuitive frontend interfaces to robust backend APIs, intelligent agent workflows, and scalable cloud infrastructure. Every layer is built for performance, reliability, and seamless integration.
Built with modularity at the core: swap AI models, scale infrastructure, or update frontends without rewriting the entire stack. Production-ready from day one.
System Flow
User Interface
Server-side rendered React application with optimistic UI updates, real-time WebSocket connections, and progressive enhancement
API Gateway & Load Balancer
AWS Application Load Balancer with path-based routing, SSL termination, and request throttling
API Layer
Async Python REST API with Pydantic validation, dependency injection, and comprehensive error handling
Containerization
Multi-stage Docker builds with layer caching, non-root users, and minimal attack surface
Container Orchestration
AWS ECS Fargate with auto-scaling policies, rolling deployments, and CloudWatch integration
AI Agent Orchestration
State-based LangGraph workflows with parallel tool execution, memory management, and streaming responses
RAG Pipeline
Hybrid search with BM25 + vector similarity, reranking, and query expansion for high-precision retrieval
Data Persistence
Multi-tier storage with S3 for documents, RDS PostgreSQL for metadata, and Redis for caching
Technology Stack
Frontend
- Next.js
- React
- TypeScript
- Tailwind CSS
Backend
- FastAPI
- Python
- RESTful APIs
- Async Processing
AI & ML
- LangChain
- LangGraph
- RAG Pipelines
- AI Agents
Infrastructure
- Docker
- Kubernetes
- AWS ECS
- Container Orchestration
Cloud
- AWS S3
- AWS Amplify
- AWS ECS Fargate
- CloudFront
Data
- Vector Databases
- PostgreSQL
- Redis Cache
- S3 Data Lake
Deployment Pipeline
Build & Containerize
Applications are packaged into Docker containers with all dependencies, ensuring consistency across environments.
Push to Registry
Container images are versioned and stored in AWS ECR for reliable artifact management.
Deploy to Cluster
Containers are deployed to Kubernetes or AWS ECS with auto-scaling and load balancing configured.
Monitor & Scale
CloudWatch metrics and logs enable real-time monitoring and automated scaling based on demand.
CI/CD Pipeline
Automated build, test, and deployment pipeline with security scanning and rollback capabilities
Source Control
- Git push triggers GitHub Actions workflow
- Branch protection rules enforce review
- Semantic versioning via commit messages
Build & Test
- Run unit tests with pytest (95%+ coverage)
- Lint with ruff and mypy for type safety
- Build Docker image with multi-stage caching
Security Scan
- Snyk container vulnerability scanning
- SAST with Bandit for Python code
- Dependency audit with pip-audit
Deploy
- Push image to AWS ECR with semantic tags
- Update ECS task definition
- Rolling deployment with health checks
Verify
- Smoke tests against production endpoints
- CloudWatch alarms monitoring
- Automatic rollback on health check failures
Security & Compliance
Multi-layered security approach with encryption, access controls, and continuous monitoring
Network Security
TLS 1.3 Encryption
End-to-end encryption for all API traffic
VPC Isolation
Private subnets for backend services
Security Groups
Least-privilege firewall rules
DDoS Protection
AWS Shield Standard + WAF rules
Authentication & Authorization
JWT Tokens
Stateless auth with RS256 signing
OAuth 2.0
Third-party authentication flows
RBAC
Role-based access control
API Key Rotation
Automated secret rotation every 90 days
Data Protection
Encryption at Rest
AES-256 for S3 and RDS
Encryption in Transit
TLS for all connections
Backup Strategy
Automated daily backups with 30-day retention
Data Masking
PII redaction in logs
Container Security
Image Scanning
Snyk + AWS ECR vulnerability scans
Non-root Users
Containers run with UID > 1000
Read-only FS
Immutable container filesystems
Resource Limits
CPU/memory quotas enforced
Monitoring & Observability
Real-time metrics and logs for application performance, infrastructure health, and cost optimization
Application Metrics
Infrastructure Metrics
AI Agent Metrics
Cost Optimization
Data Flow Patterns
Efficient data processing patterns for synchronous requests, RAG retrieval, and asynchronous jobs
Request/Response Flow
Synchronous API requests with streaming responses for LLM outputs
RAG Retrieval Flow
Hybrid search combining dense vectors and keyword matching for optimal precision
Async Background Jobs
Event-driven processing for long-running tasks using SQS queues
Architectural Best Practices
Core principles guiding system design and implementation
Modularity
Loosely coupled components enable independent scaling and technology swaps without system-wide rewrites
Observability
Comprehensive logging, metrics, and tracing across all layers for rapid debugging and performance tuning
Fault Tolerance
Graceful degradation with circuit breakers, retries, and fallback mechanisms to handle partial failures
Horizontal Scaling
Stateless services and auto-scaling policies allow seamless capacity expansion during traffic spikes
Security by Design
Defense in depth with encryption, least-privilege access, and continuous security scanning at every layer
Performance First
Async processing, connection pooling, and multi-level caching minimize latency and maximize throughput
Infrastructure as Code
All infrastructure is version-controlled using Terraform and AWS CDK, enabling reproducible deployments and infrastructure rollbacks. Configuration is immutable—changes require new deployments rather than in-place modifications.