MLOps & Platform Engineer

Astek Group; Riyadh; On-site; SAR 200,000 - 300,000; Full time

Job summary

A technology solutions provider in Riyadh is seeking an experienced MLOps / Platform Engineer to design and implement secure deployment pipelines for machine learning workloads. In this hands-on role, you'll manage Kubernetes clusters and develop CI/CD pipelines, ensuring seamless automation through to deployment. The successful candidate will have experience in Kubernetes, Docker, and CI/CD practices, and a strong mindset for ownership and collaboration. This opportunity allows you to work closely with AI engineers to elevate prototypes into scalable solutions.

Qualifications

• 3–8 years in Platform Engineering, DevOps, or Site Reliability Engineering (SRE).
• At least 2 years of experience with Kubernetes in a production environment.
• Proficient in Docker, CI/CD practices, and Linux.

Responsibilities

• Operate and manage Kubernetes clusters in production.
• Develop and maintain CI/CD pipelines using tools like Jenkins.
• Implement observability practices to quickly identify and resolve issues.
• Deploy ML models and services, ensuring high performance.
• Work closely with AI engineers to transition prototypes into production.

Skills

Platform Engineering

Kubernetes

Docker

CI/CD

Linux

GitOps

Infrastructure as Code

Machine Learning deployment

Job description

We are looking for an MLOps / Platform Engineer. In this hands‑on role, you'll design and implement secure, scalable deployment pipelines in on‑premises and private cloud environments, directly supporting machine learning workloads and GenAI solutions.

Key Responsibilities

• Operate and manage Kubernetes clusters in production, designing deployments for maximum efficiency and reliability.
• Develop and maintain CI/CD pipelines using tools like Jenkins, ensuring seamless automation from build to deployment.
• Implement observability practices, including logging and metrics, to quickly identify and resolve issues.
• Deploy ML models and services, ensuring high performance and scalability while optimizing inference processes.
• Work closely with AI engineers to transition prototypes into production‑grade deployments and maintain thorough documentation.

Requirements

• 3–8 years in Platform Engineering, DevOps, or Site Reliability Engineering (SRE).
• At least 2 years of experience with Kubernetes in a production environment.
• Proficient in Docker, CI/CD practices, and Linux.
• Knowledge of GitOps tools (e.g., ArgoCD, Flux) and Infrastructure as Code (Terraform/Ansible).
• Experience deploying machine learning workloads and familiarity with GenAI technologies.
• Strong ownership mindset with excellent documentation and collaboration skills.
• Ability to work directly with clients and navigate complex environments.