DevOps Engineer

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

Job summary

A leading technology firm in Riyadh is seeking a DevOps Engineer to oversee deployment, scalability, and security of AI systems. This critical role involves building CI/CD pipelines, optimizing infrastructure, and ensuring compliance with data governance standards. The ideal candidate will have at least 5 years of relevant experience and proven expertise in deploying AI workloads. Strong skills in Docker, Kubernetes, and cloud services are essential, alongside a commitment to security best practices.

Qualifications

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

Responsibilities

• Design, build, and maintain CI/CD pipelines for AI models and software applications.
• Deploy and scale AI systems and LLM APIs in production environments.
• Monitor infrastructure performance and optimize for reliability and cost efficiency.

Skills

DevOps engineering experience

Deployment and operation of AI systems

Hands-on expertise with Docker

Experience with Kubernetes

CI/CD platforms knowledge

Cloud services proficiency

Monitoring and observability

Infrastructure-as-code tools

Troubleshooting capabilities

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.
• Mandatory: 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.