AI Engineer with a Computer Science + Business Administration background, bridging technical innovation and business strategy to deliver production-ready solutions that actually ship.
I'm passionate about translating complex AI challenges into scalable, production-grade systems that drive measurable value.
I combine critical thinking with hands-on execution, building agentic workflows, MLOps pipelines, and full-stack AI applications that solve high-impact problems at scale.
Business-Aligned AI Engineering
- Translate business requirements into architectures that deliver measurable value
- Balance innovation with pragmatism—choosing the right tool for value creation, not hype
- Cut through AI buzzwords to what actually works in production
Production AI Systems
- Agentic automation flows with MCP servers that reduce operational overhead
- MLOps pipelines: training, validation, deployment, monitoring at scale
- Full-stack AI applications with scalable API architectures
- LLMOps governance: prompt versioning, budget controls, RBAC
Infrastructure & DevOps
- Cloud-native AI infrastructure (Terraform, GCP, Kubernetes)
- CI/CD pipelines for AI systems (GitHub Actions)
Experimentation
- Evaluate SOTA frameworks (LangGraph, multi-agent) for production viability
- Balance performance with cost—no trend-chasing
