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LLM integrations, real-time services, and AI-driven backends led end to end.
The challenge
Turn language models and automation into dependable product surfaces—backends, interfaces, and delivery paths that hold up beyond the prototype.
My contribution
- Architecture and delivery of Python (Django) and Go backends for AI-driven apps
- LLM/automation integrations and real-time WebSocket services
- Docker, Traefik/Nginx, SSL, DNS, and CI/CD ownership; mentoring and code reviews
AI as part of the product
At Ai Run, I lead architecture and delivery of backends for AI-driven applications and integrate language models and NLP into working products. The focus is connecting useful model capabilities to the services, interfaces, and workflows around them—while setting coding standards and running reviews.
From prototype to delivery
Python, Django, and Go provide the application foundation. Streamlit and n8n support interfaces and automation; WebSockets power real-time services. Docker, Traefik/Nginx, SSL, DNS, and CI/CD keep deployment and operation repeatable in production.