Jalil Seidu, Developer in Tokyo, Japan
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Jalil Seidu

Bio

Jalil is a cloud and DevOps infrastructure engineer specializing in Kubernetes platforms, Terraform automation, and multi-cloud environments across Google Cloud, AWS, and Azure. He has built and operated large-scale infrastructure supporting GPU-accelerated machine learning workloads, CI/CD platforms, and highly scalable cloud systems that improve reliability, performance, and deployment speed.

Portfolio

Rakuten
Linux, Google Cloud Platform (GCP), Azure, GPU Computing, GitHub Actions...
DreamWorks Animation
OpenShift, Terraform, Linux, Ansible, OpenStack, CI/CD Pipelines, Kubernetes...
dv01
Google Cloud Platform (GCP), Terraform, Linux, GitHub Actions, Helm...

Experience

  • Kubenetes - 10 years
  • Terraform - 10 years
  • Docker - 10 years
  • Helm - 8 years
  • Argo CD - 5 years
  • Ansible - 5 years
  • GitHub Actions - 4 years
  • NVIDIA CUDA - 2 years

Preferred Environment

Linux, Kubenetes, Docker, Terraform, Google Cloud Platform (GCP), Azure, Helm, GitHub Actions, GitLab CI/CD, Argo CD

The most amazing...

...work I've done is design and deploy Kubernetes infrastructure on Google Cloud using Terraform, cutting costs by 30% while improving deployment reliability.

Work Experience

DevOps & Server Engineer – GPUOD

2024 - PRESENT
Rakuten
  • Integrated CI/CD pipelines using GitHub Actions and containerization with Docker into machine learning workflows.
  • Operated and optimized Kubernetes clusters for GPU-accelerated machine learning workloads, ensuring scalability, reliability, and security.
  • Optimized deep learning training efficiency by fine-tuning NVIDIA CUDA kernels and implementing Multi-Instance GPU (MIG) profiles, maximizing hardware utilization across shared enterprise clusters.
  • Maintained internal tooling and comprehensive documentation for machine learning infrastructure and best practices.
  • Architected and managed production-grade OpenShift clusters, leveraging the NVIDIA GPU Operator to automate driver lifecycle management and ensure high availability for mission-critical AI services.
Technologies: Linux, Google Cloud Platform (GCP), Azure, GPU Computing, GitHub Actions, Terraform, Python, Azure DevOps, CI/CD Pipelines, Azure DevOps Services, Kubernetes, Grafana, Prometheus, Docker, DevOps, Continuous Delivery (CD), Infrastructure as Code (IaC), Continuous Integration (CI), Bash, Linux Administration, PostgreSQL, Redis, Node.js, NVIDIA CUDA, OpenShift

DevOps Engineer - Kubernetes/Openshift Ecosystem

2022 - 2025
DreamWorks Animation
  • Introduced Terraform and converted Puppet manifests to Ansible roles, reducing build time by 60% for DreamWorks Animation Nova clients on OpenStack.
  • Maintained the team’s OpenShift platform to support and run applications dependent on highly available and highly scalable infrastructure.
  • Implemented centralized logging and monitoring with Splunk, enabling faster troubleshooting and improved operational visibility across OpenShift infrastructure.
Technologies: OpenShift, Terraform, Linux, Ansible, OpenStack, CI/CD Pipelines, Kubernetes, Docker, DevOps, Infrastructure as Code (IaC), Continuous Integration (CI), Bash, Linux Administration

Senior Cloud Engineer

2020 - 2024
dv01
  • Designed and deployed scalable Kubernetes clusters on Google Cloud using Terraform and Helm, reducing infrastructure costs by 30%.
  • Collaborated with cross-functional teams to optimize cloud architecture for performance and cost-efficiency, increasing application performance by 25%.
  • Built, managed, supported, and improved tools for continuous integration, automated performance and stress testing, and release management.
Technologies: Google Cloud Platform (GCP), Terraform, Linux, GitHub Actions, Helm, CI/CD Pipelines, Kubernetes, Grafana, Prometheus, Docker, DevOps, Continuous Delivery (CD), Infrastructure as Code (IaC), Continuous Integration (CI), Amazon Web Services (AWS), GCP DevOps, Bash, Linux Administration, PostgreSQL, Redis, TypeScript

Senior DevOps Engineer

2018 - 2020
Omise
  • Planned, designed, and implemented the successful migration of workloads from AWS to Google Cloud, reducing operational costs by 20%.
  • Constructed Docker containers to break up monolithic apps into microservices, optimizing developer workflow, increasing scalability, and improving speed.
  • Managed Linux infrastructure for payment processing systems and successfully supported annual PCI DSS audits, ensuring compliance with security standards through system hardening, monitoring, and access control.
Technologies: AWS IoT, Google Cloud Platform (GCP), Terraform, Python, Flux CD, Helm, CI/CD Pipelines, Kubernetes, Grafana, Prometheus, Docker, DevOps, Continuous Delivery (CD), Infrastructure as Code (IaC), Continuous Integration (CI), Amazon EC2, Bash, Linux Administration, PostgreSQL, MongoDB

Experience

InferScale – Scalable AI Inference Infrastructure Platform

https://inferscale.netlify.app/
Designed a conceptual AI inference platform that demonstrates scalable cloud architecture for machine learning workloads. The project outlines infrastructure components such as Kubernetes-based inference services, CI/CD pipelines, and automated infrastructure provisioning using Terraform.

Certifications

NOVEMBER 2020 - DECEMBER 2022

GCP Associate Cloud Engineer

Google Cloud

SEPTEMBER 2020 - OCTOBER 2022

GCP Professional Cloud Architect

Google Cloud

Skills

Libraries/APIs

Node.js

Tools

Terraform, Helm, GitLab CI/CD, Google Kubernetes Engine (GKE), Ansible, Azure DevOps Services, Grafana

Platforms

Google Cloud Platform (GCP), Kubernetes, Linux, Docker, Azure, AWS IoT, OpenShift, OpenStack, Amazon EC2, Amazon Web Services (AWS), NVIDIA CUDA, Cloud Run

Languages

Python, Bash, TypeScript

Paradigms

DevOps, Azure DevOps, Continuous Delivery (CD), Continuous Integration (CI)

Storage

PostgreSQL, Redis, Google Cloud SQL, MongoDB

Other

Kubenetes, GitHub Actions, Argo CD, GPU Computing, Flux CD, GCP DevOps, CI/CD Pipelines, Prometheus, Infrastructure as Code (IaC), Linux Administration, Artificial Intelligence (AI), Containers, Computer Engineering, Cloud Storage, Cloud Load Balancing, Compute Architecture, Storage architecture decisions, Network architecture design, Deploying applications on Google Cloud, Managing virtual machines, Deploying containerized applications

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