AI-Native DevOps Engineer

About This Role

Modern DevOps is no longer just about automating deployments, it's about building intelligent, cloud-native platforms that power the next generation of AI-enabled enterprise applications. You'll work across cloud engineering, Kubernetes, Infrastructure as Code, CI/CD, observability, and AI infrastructure to ensure enterprise systems are resilient, secure, and always available. Working closely with Software Engineers, AI Engineers, Data Scientists, Security teams, and Product Managers, you'll help organisations accelerate innovation while maintaining operational excellence.

What You Will Do

• Design, implement, and manage cloud-native infrastructure across AWS, Azure, or Google Cloud Platform
• Build and maintain automated CI/CD pipelines that support rapid, secure, and reliable software delivery
• Deploy and manage containerised applications using Kubernetes and Docker
• Provision infrastructure using Infrastructure as Code (Terraform, CloudFormation, or similar tools)
• Enable scalable deployment and lifecycle management of AI-enabled applications and services
• Implement monitoring, logging, tracing, and observability solutions for enterprise platforms
• Collaborate with Engineering, AI, Security, and Product teams to improve deployment velocity and platform reliability
• Automate operational processes to improve scalability, resilience, and efficiency
• Implement DevSecOps practices including security scanning, secrets management, and compliance automation
• Continuously optimise cloud infrastructure for performance, cost efficiency, and operational excellence

What You Need to Succeed

• 5-10 years of DevOps experience understanding the infrastructure requirements of modern AI-enabled applications
• Container and orchestration expertise: Docker, Kubernetes, and Helm
• Infrastructure as Code proficiency with Terraform, plus at least one cloud-native templating tool
• CI/CD ownership across Jenkins, GitHub Actions, Azure DevOps, or GitLab CI/CD
• Experience deploying and managing AI/ML or LLM-powered applications, including GPU-enabled cloud infrastructure and containerised AI inference services
• Monitoring and observability tooling experience, Prometheus, Grafana, or equivalent

What Will Give You an Edge

• MLOps and LLMOps fundamentals, including vector database deployment and management
• AI application observability and monitoring experience
• Prompt engineering fundamentals and AI-assisted DevOps workflows, GitHub Copilot, Cursor, or similar
• Cost optimisation experience for AI infrastructure and cloud resources

What Qfyre Offers

• Genuine infrastructure ownership powering AI-enabled enterprise applications, not a support function
• Exposure to GPU-enabled, cutting-edge AI infrastructure at enterprise scale
• Competitive compensation reflecting current market demand for this profile
• Flexible work arrangement, Onsite, Hybrid or Remote depending on the engagement

Skills and Technologies

KubernetesDockerTerraformCI/CDMLOpsAI Infrastructure

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