DevOps Engineer

Tarjama&; Saudi Arabia; On-site; SAR 120,000 - 180,000; Full time

Job summary

A technology solutions company in Saudi Arabia is seeking a skilled DevOps Engineer responsible for the deployment, scalability, and reliability of AI systems. The ideal candidate will have a strong background in CI/CD pipelines, AI deployments, and cloud-native infrastructures. Key responsibilities include optimizing infrastructure performance and ensuring compliance with security standards. The role requires extensive experience with Docker, Kubernetes, and monitoring tools.

Qualifications

• Minimum 5 years of hands‑on DevOps engineering experience in production environments.
• Proven experience deploying and operating AI systems and LLM‑based workloads in production.
• Strong understanding of networking, security, and cloud‑native architecture principles.

Responsibilities

• Design, build, and maintain CI/CD pipelines for AI models.
• Deploy, operate, and scale AI systems and cloud-based services.
• Collaborate with AI Engineers and other teams for seamless deployments.

Skills

DevOps engineering

Docker

Kubernetes

CI/CD platforms

Cloud services

Troubleshooting

Tools

Terraform

Monitoring tools

Observability tools

Job description

The DevOps Engineer will play a mission-critical role owning the deployment, scalability, security, and reliability of AI systems and digital platforms. This role has a strong focus on LLM deployments, AI workloads, and cloud-native infrastructure, ensuring that all AI and software systems operate with enterprise-grade availability, performance, and compliance.

Key Responsibilities

CI/CD & Automation Engineering

Design, build, and maintain CI/CD pipelines for AI models, LLM services, and software applications. Automate build, test, deployment, and environment configuration workflows to enable rapid and reliable releases.

AI & LLM Deployment Operations

Deploy, operate, and scale AI systems, LLM APIs, inference workloads, and cloud-based AI services. Ensure high availability, horizontal scalability, and low-latency inference across all production environments.

Infrastructure, Reliability & Cost Optimization

Monitor infrastructure performance, system health, and AI workloads using observability and monitoring tools. Optimize infrastructure for reliability, performance, and cloud cost efficiency.

Security, Compliance & Governance

Implement and enforce security best practices, access controls, secrets management, and environment isolation. Ensure infrastructure and deployment processes align with national data governance, compliance, and cybersecurity standards.

Cross-Functional Enablement

Collaborate closely with AI Engineers, Full-Stack Engineers, and Product teams to enable seamless, scalable deployments. Act as the primary technical owner for production reliability during mission-critical deployments.

Documentation & Architecture Standards

Maintain comprehensive documentation for DevOps workflows, system architecture, environments, and deployment standards. Ensure operational readiness, auditability, and knowledge transfer across teams.

Required Qualifications

• Minimum 5 years of hands‑on DevOps engineering experience in production environments.
• Proven experience deploying and operating AI systems and LLM‑based workloads in production.
• Strong hands‑on expertise with Docker, Kubernetes, CI/CD platforms, and cloud services.
• Experience with monitoring, observability, logging, and infrastructure‑as‑code (e.g., Terraform, similar tools).
• Strong understanding of networking, security, and cloud‑native architecture principles.
• Excellent troubleshooting and incident response capabilities in high‑availability systems.

Preferred Qualifications

• Experience with MLOps platforms such as MLflow, SageMaker, Vertex AI, or similar.
• Proven experience scaling AI and LLM applications in high‑traffic production environments.
• Exposure to AI model lifecycle management, retraining pipelines, and operational governance.
• Experience in government, regulated, or national‑scale enterprise environments.

KPIs & Deliverables

• Uptime, reliability, and stability of AI platforms and production systems.
• Deployment speed, automation maturity, and release reliability.
• Infrastructure performance, scalability, and cost optimization efficiency.
• Security posture and compliance readiness across all environments.
• Quality, completeness, and audit readiness of DevOps documentation and workflows.