Designed backend components and REST APIs around AI pipelines, structured files, metadata, execution logs and validation.
Platform engineering · AI integration
Modular AI Platform
A modular software platform for workflow orchestration, structured data, execution tracking and AI/LLM service integration, built around clear service boundaries and automated delivery.
- Role
- Full-Stack Developer · Architecture, DevOps & AI Integration
- Organization
- Modular AI Platform
- Period
- 2024—Present
02
Inputs→API + validation→Orchestration→AI / tools→State + logs
01 · Context & problem
Modular AI Platform
AI features become difficult to operate when model calls, file handling, validation and execution state are tightly coupled. The platform work focused on separating those concerns so workflows can evolve without destabilizing the rest of the product.
02 · What I built
Helped decouple services so modules can evolve independently and remain easier to test and maintain.
Integrated AI models and services into reproducible workflows with explicit input, output, error and execution-state handling.
Implemented storage, progress tracking and asynchronous processing, reinforced by containers, automated tests and CI/CD.
03 · System flow
01Inputs
→02API + validation
→03Orchestration
→04AI / tools
→05State + logs
04 · Engineering decisions
Software boundaries before model novelty
Model providers and AI steps sit behind application contracts so the surrounding product is not tied to one provider.
Execution state is product data
Inputs, outputs, errors, metadata and progress are explicit state that can be inspected and validated.
Human review around agentic tooling
Coding assistants are used for analysis, refactoring, tests and documentation with human review and CI/CD validation.
05 · Stack
PythonFastAPIREST APIsSQLDockerGitLab CI/CDPyTorchOpenCVRAGMCPTool callingLinux
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Contact
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