Qureos Mentor

Tarjama&

<div>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 &amp; Automation Engineering<ul>
<li>
Design, build, and maintain CI/CD pipelines for AI models, LLM services, and software applications.</li>
<li>Automate build, test, deployment, and environment configuration workflows to enable rapid and reliable releases.</li>
</ul>
AI &amp; LLM Deployment Operations<ul>
<li>
Deploy, operate, and scale AI systems, LLM APIs, inference workloads, and cloud-based AI services.</li>
<li>Ensure high availability, horizontal scalability, and low-latency inference across all production environments.</li>
</ul>
Infrastructure, Reliability &amp; Cost Optimization<ul>
<li>
Monitor infrastructure performance, system health, and AI workloads using observability and monitoring tools.</li>
<li>Optimize infrastructure for reliability, performance, and cloud cost efficiency.</li>
</ul>
Security, Compliance &amp; Governance<ul>
<li>
Implement and enforce security best practices, access controls, secrets management, and environment isolation.</li>
<li>Ensure infrastructure and deployment processes align with national data governance, compliance, and cybersecurity standards.</li>
</ul>
Cross-Functional Enablement<ul>
<li>
Collaborate closely with AI Engineers, Full-Stack Engineers, and Product teams to enable seamless, scalable deployments.</li>
<li>Act as the primary technical owner for production reliability during mission-critical deployments.</li>
</ul>
Documentation &amp; Architecture Standards<ul>
<li>
Maintain comprehensive documentation for DevOps workflows, system architecture, environments, and deployment standards.</li>
<li>Ensure operational readiness, auditability, and knowledge transfer across teams.</li>
</ul>
Required Qualifications<ul>
<li>
Minimum 5 years of hands-on DevOps engineering experience in production environments.</li>
<li>Mandatory: Proven experience deploying and operating AI systems and LLM-based workloads in production.</li>
<li>Strong hands-on expertise with Docker, Kubernetes, CI/CD platforms, and cloud services.</li>
<li>Experience with monitoring, observability, logging, and infrastructure-as-code (e.g., Terraform, similar tools).</li>
<li>Strong understanding of networking, security, and cloud-native architecture principles.</li>
<li>Excellent troubleshooting and incident response capabilities in high-availability systems.</li>
</ul>
Preferred Qualifications<ul>
<li>
Experience with MLOps platforms such as MLflow, SageMaker, Vertex AI, or similar.</li>
<li>Proven experience scaling AI and LLM applications in high-traffic production environments.</li>
<li>Exposure to AI model lifecycle management, retraining pipelines, and operational governance.</li>
<li>Experience in government, regulated, or national-scale enterprise environments.</li>
</ul>
KPIs &amp; Deliverables<ul>
<li>
Uptime, reliability, and stability of AI platforms and production systems.</li>
<li>Deployment speed, automation maturity, and release reliability.</li>
<li>Infrastructure performance, scalability, and cost optimization efficiency.</li>
<li>Security posture and compliance readiness across all environments.</li>
<li>Quality, completeness, and audit readiness of DevOps documentation and workflows.</li></ul></div>

DevOps Engineer

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Tarjama&

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DevOps Engineer