I'm an AI Engineer with an infrastructure backbone — I build LLM-powered systems and deploy them to production. Most AI engineers can do one; my edge is doing both.
What I'm working on:
- Deploying and operating LLM observability (self-hosted Langfuse on Kubernetes) for production AI assistants — tracing, prompt management, evals, and cost tracking
- Building RAG pipelines and AI agents with evaluation baked in from day one
- 3+ years running production infrastructure: Kubernetes (AKS), Terraform, GitLab CI/CD, ArgoCD, Azure & AWS
Background:
- 10 years teaching Computer Science (Programming for AI, Distributed Computing, Cloud Computing) — I explain complex systems clearly, to engineers and stakeholders alike
- DevOps/MLOps consultant for enterprise clients — 20+ microservices in production, monthly → daily release cadence
Currently deepening: advanced RAG patterns, LangGraph agents, AI evaluation frameworks (Ragas, Langfuse) — through a structured 43-week Applied AI Engineer program, building production-grade projects along the way.
Classroom → production infrastructure → AI systems. Every transition ran on the same principle: I may not know the answers, but I trust myself to figure it out.
📫 Reach me: asadhanif3188@gmail.com | LinkedIn