I build resilient, scalable cloud platforms and reliable infrastructure for GenAI workloads — with Kubernetes, AWS and Terraform. Site Reliability Engineer based in Southfield, Michigan.
I'm an AI Infrastructure and Site Reliability Engineer with 3+ years designing and operating large-scale distributed systems, cloud-native platforms, and Kubernetes-based AI/ML infrastructure. Expertise in AWS, Kubernetes, Terraform, Docker, and CI/CD — with a strong focus on scalability, reliability, and performance optimization.
Proven track record improving system availability, reducing latency, and automating infrastructure using IaC. Experienced in GenAI infrastructure, model deployment pipelines, and platform engineering — with hands-on experience in observability, incident response, SLA/SLO management, and production-grade system design. Founder of Hamuzair; Master's in Data Analytics from Indiana Wesleyan University.
My core infrastructure toolkit and the path that got me here.
Coinbase · Full-time · USA
CirrusLabs · Full-time · IND
Hamuzair
Indiana Wesleyan University
Shadan College of Engineering and Technology
Designing and operating scalable, secure cloud platforms on AWS — built for performance across multiple regions.
Observability, incident response and automation that keep production systems fast, stable and highly available.
Container orchestration, autoscaling and zero-downtime deployments with Kubernetes for resilient workloads.
Reliable, optimized infrastructure for GenAI and ML workloads — from inference pipelines to cost optimization.
Representative work across cloud reliability, GenAI infrastructure and automation. Filter by focus area.
Cloud-native observability for Kubernetes with centralized logging and proactive alerting — automated provisioning cut configuration effort 45% and auto-remediation reduced incident response time 38%.
Production-grade EKS clusters with autoscaling and zero-downtime rollouts; reduced provisioning time 35% and improved deployment reliability.
GenAI-driven system that detects idle resources and optimizes compute — improving efficiency 25% with real-time CloudWatch monitoring and automated dashboards.
Automated model-serving and deployment pipelines for AI/ML workloads, with rollout automation, monitoring and feature-store concepts.
Secure, automated CI/CD that cut manual deployment effort 45%, with DevSecOps compliance checks built directly into the pipeline.
Reusable Infrastructure-as-Code templates provisioning multi-account AWS infrastructure, reducing provisioning time 50%.
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Have a project, role or idea in mind? Drop me a message.