System Architecture & Design

Problem

Complex multi-pipeline AI platforms require clear architectural boundaries to remain maintainable, testable, and extensible across teams and workflows.

Solution

Designed a layered architecture with a central orchestrator, DI container, strategy pattern for backend execution, and adapter protocols that decouple business logic from external providers like AWS, OpenAI, and Google Cloud.

Impact

• →Established clear separation between orchestration, logic, and infrastructure layers
• →Enabled 15+ compliance workflows to share pipelines, services, and configuration
• →Decoupled external provider integrations through adapter protocols and lazy initialization

Architecture

1. 01Application Orchestrator (app.py) bootstraps the DI container and runtime state
2. 02BackendExecutor uses the strategy pattern to unify AI backend calls
3. 03Logic Layer organizes Pipelines, Services, and Config for 15+ compliance workflows
4. 04Adapters & Protocols (ApiHandler, BedrockFormatter) normalize external communication
5. 05RagFactory and lazy-initialized AWS/OpenAI clients serve the infrastructure layer
6. 06External providers (AWS, OpenAI, Google Cloud) are accessed through decoupled integrations

Capabilities

• ·Application orchestration with DI container
• ·Strategy pattern for unified backend execution
• ·Adapter and protocol layer for API handlers
• ·RagFactory for retrieval integration
• ·Lazy initialization of cloud clients
• ·Multi-provider external integration