I integrate AI and large language models into real production applications.
My focus is practical AI engineering: connecting models to business workflows, APIs, databases, documents, user interfaces, and existing SaaS systems rather than experimental model research.
I work with Python, FastAPI, JavaScript, Node.js, Gemini, OpenAI-compatible APIs, document processing, structured outputs, workflow automation, and cloud deployment.
I work with:
- LLM API integration
- AI-powered SaaS features
- Gemini integrations
- OpenAI API integrations
- Claude API integrations
- AI-assisted document processing
- Structured AI outputs
- Prompt and workflow design
- AI-powered search and question answering
- RAG-style applications
- AI agents and tool calling
- Document extraction and classification
- OCR workflow integration
- AI-generated reports
- Conversational interfaces
- AI workflow automation
- Backend APIs for AI features
- Python and FastAPI AI services
- Model response validation
- Retry and fallback strategies
- Usage and cost monitoring
- AI feature integration into existing React/Next.js products
I have implemented AI features in production software, including healthcare documentation workflows, AI-assisted analysis, natural-language interactions, and integrations between LLMs and existing application data.
I pay particular attention to the parts that make AI features usable in production: validation, error handling, structured responses, permissions, latency, cost control, observability, and safe fallbacks when a model response is uncertain or unavailable.
I can build a standalone AI feature or integrate AI into an existing SaaS product.