📍 Toronto, Ontario, Canada
I am a B.Sc. Computer Engineering graduate from Isfahan University of Technology (IUT), currently based in Toronto, Ontario.
I am deeply interested in DevOps, MLOps, infrastructure engineering, cloud-native systems, and software deployment.
My long-term goal is to become an Infrastructure Engineer or Platform Engineer, working on the design, deployment, automation, and operation of scalable, reliable, and production-grade systems.
I am especially interested in:
- DevOps and modern infrastructure
- Kubernetes and container orchestration
- Docker and containerized applications
- CI/CD pipelines
- Linux and networking
- Monitoring and observability
- Infrastructure as Code
- Cloud computing
- AI model deployment and serving
- MLOps and ML lifecycle management
- GPU infrastructure and AI workloads
- Distributed systems
- Cloud-native architecture
I enjoy learning new technologies through hands-on projects and real-world environments.
One of my strongest interests is understanding how different tools and components fit together, why each tool exists, and where it should be used in a production architecture.
I am currently focusing on:
- Kubernetes
- GitHub Actions
- Linux
- Terraform
- AWS
- Model Deployment
I am also learning AWS to better understand how production infrastructure is designed in cloud environments, especially around:
- Compute
- Networking
- Storage
- IAM
- Infrastructure as Code
My goal is not just to learn individual tools, but to understand how they interact inside real production systems.
Alongside DevOps and infrastructure, I am highly interested in AI systems and model deployment.
I am especially curious about what happens after a model is trained:
- How it is packaged
- How it is versioned
- How it is deployed
- How it is served
- How it is scaled
- How GPU resources are managed
- How it is monitored in production
I currently work with and experiment with tools such as:
- Triton Inference Server
- vLLM
- KServe
- MLflow
- Docker
- Kubernetes
My main interest is understanding how real-world AI infrastructure is designed and operated at scale.
- Python
- Go
- C
- C++
- Bash
- Docker
- Kubernetes
- Linux
- Git
- GitHub
- Nginx
- CI/CD
- AWS — Currently Learning
- Prometheus
- Grafana
- OpenTelemetry
- Terraform
- Ansible
- PostgreSQL
- Flask
- REST APIs
- MLflow
- KServe
- Triton Inference Server
- vLLM
- Model Serving
- Model Deployment
- GPU Workloads
- AI Inference
- DevOps
- MLOps
- Infrastructure Engineering
- Platform Engineering
- Cloud Computing
- AWS
- Kubernetes
- Linux
- Networking
- Distributed Systems
- AI Infrastructure
- GPU Computing
- Model Deployment
- Observability
- System Reliability
I strongly believe in learning by building.
Instead of only studying tools theoretically, I prefer creating practical environments where I can experiment with them.
Some examples include:
- Building Kubernetes environments
- Containerizing applications
- Deploying backend and frontend services
- Creating CI/CD pipelines
- Setting up monitoring and observability
- Serving AI models
- Working with GPU workloads
- Experimenting with KServe
- Working with MLflow
- Exploring production-like infrastructure
I always try to understand:
- Why a tool exists
- What problem it solves
- What alternatives exist
- Where it fits in a production system
- How it interacts with other components
- What limitations it has
- How it behaves at scale
My goal is to grow from someone who knows how to use tools into an engineer who can understand, design, and make decisions about infrastructure architecture.