Seyedhashem Hamzehzadeh

A capable, motivated, accurate, and adaptable individual who loves challenging environments. Ability to manage time efficiently and work well individually and as a team.
Reliable, hardworking, quick learner, able to adapt to new work environments and projects.

- Masters of Science in Wireless and Computer Communication Networks, London, UK.
- Masters of Science in Information Technology (Advanced Computer Networks and Information
Security), IRAN.

Recent works: 

- Deployed and ran Kubernetes cluster (on-premise) on bare metal (Master node and 2 GPU worker nodes) [CRI-O container runtime, Calico Pod Network, MetalLB LoadBalancer, NVIDIA toolkit, NVIDIA device plugin, GPU-Operator].

- Deployed Kubeflow on GPU worker node. [Kustomize control command, OpenEBS].

- Managing and Maintaining Kubernetes cluster, Kubeflow, Kubeflow pipeline (KFP), KServe, MLFlow(Deployed on the K8s cluster and connected to Kubeflow), MinIO, Velero.

- Deployed the MinIO operator inside the K8s cluster and created the necessary Tenants; Connected the MLflow and Kubeflow mlpipline to the created Tenant to utilize the S3 Buckets as an artifact storage instead of local PVCs.

- Implemented MIG Single strategy and Time-Slicing configuration on NVIDIA GPUs on the K8s cluster.

- Exposed Kubflow UI with HTTPS enabled using MetalLB load balancer, Kubernetes secret, istio-ingress-gateway, and Kubeflow gateway.

- Exposed MLflow UI and MinIO operator console UI with enabled HTTPS utilizing ingress nginx.

- Migrated data stored in the PVCs, from the Liqid Honey Badger to the local disk on the Dell server worker node, on the k8s cluster.

- Deployed Velero on the K8s cluster, took a full backup, and scheduled time-specified backups.

- Set up and created new custom Docker images for utilization in Kubeflow.

Other experiences:
- ISO 27001 implementation and auditing and risk management
- Vulnerability Assessment
- Experienced in AI, Deep Learning, Data Centre, Networking and Network Security, Coding (Python, MATLAB).

- Deployed and ran Kubernetes cluster (on-premise) on bare metal (Master node and 2 GPU worker nodes) 
[CRI-O container runtime, Calico Pod Network, MetalLB LoadBalancer, NVIDIA toolkit, NVIDIA device plugin, GPU-Operator].
- Deployed Kubeflow on GPU worker node. [Kustomize control command, OpenEBS].
- Managing and Maintaining Kubernetes cluster, Kubeflow, Kubeflow pipeline (KFP), KServe, MLFlow(Deployed on the K8s cluster and connected to Kubeflow), MinIO, Velero.
- Deployed the MinIO operator inside the K8s cluster and created the necessary Tenants; Connected the MLflow and Kubeflow mlpipline to the created Tenant to utilize the S3 Buckets as an artifact storage instead of local PVCs.
- Implemented MIG Single strategy and Time-Slicing configuration on NVIDIA GPUs on the K8s cluster.
- Exposed Kubflow UI with HTTPS enabled using MetalLB load balancer, Kubernetes secret, istio-ingress-gateway, and Kubeflow gateway.
- Exposed MLflow UI and MinIO operator console UI with enabled HTTPS utilizing ingress nginx.
- Migrated data stored in the PVCs, from the Liqid Honey Badger to the local disk on the Dell server worker node, on the k8s cluster.
- Deployed Velero on the K8s cluster, took a full backup, and scheduled time-specified backups.
- Set up and created new custom Docker images for utilization in Kubeflow.

Master Degree Wireless and Computer Communication Networks Engineer at Brunel University London

Brunel’s Wireless and Computer Communication Networks MSc will deepen students theoretical understanding of wireless and computer communication systems, as well as equip them with the ability to analyse complex problems associated with such systems.
Students will study and work to solve complex industrial problems in wireless and computer communications. This specialist course has a particular emphasis on traditional, new and emerging technologies and subjects, including:

Communication network technologies 
Communication network security
Intelligent systems
Internet of things 
Radio and optical communication systems
Business and enterprise
Embedded DSP (Digital Signal Processing)
Engineering ethics and sustainability