Automated AI Infrastructure Specialist

4 days ago

Dublin, Dublin City, Ireland beBeeDevops Full time €100,000 - €150,000
Staff AI DevOps Engineer Job Summary

We are seeking a highly skilled Staff AI DevOps Engineer to join our dynamic team. As a key member of our engineering team, you will be responsible for building the automated backbone of our AI platform, driving continuous integration and delivery (CI/CD), infrastructure as code (IaC), and observability to enable secure, scalable, and rapid deployment of intelligent agents.

This role requires expertise in designing and maintaining CI/CD pipelines, implementing IaC and GitOps practices, building and managing Kubernetes-based infrastructure, integrating machine learning operations (MLOps) tools and workflows, deploying and maintaining observability stacks, and enforcing DevSecOps practices.

Main Responsibilities
  1. Design and maintain CI/CD pipelines to support rapid, reliable delivery of AI platform components and intelligent agents.
  2. Implement IaC and GitOps practices to manage scalable, secure, and reproducible environments.
  3. Build and manage Kubernetes-based infrastructure, including custom operators and GPU workload orchestration.
  4. Integrate MLOps tools and workflows to streamline the AI model lifecycle from training to deployment.
  5. Deploy and maintain observability stacks for real-time monitoring, tracing, and alerting across distributed systems.
  6. Enforce DevSecOps practices, including automated security scanning, policy-as-code, and compliance automation.
Requirements
  1. 5-7+ years of experience in DevOps, site reliability engineering (SRE), or platform engineering.
  2. Expertise in CI/CD pipeline design and tools (e.g., Jenkins, GitLab CI, ArgoCD, GitHub Actions).
  3. Deep understanding of IaC (e.g., Terraform, CloudFormation).
  4. Expert-level Kubernetes administration and workload management, including custom controllers/operators.
  5. Experience with MLOps tools and platforms (e.g., Kubeflow, MLflow, Seldon Core, Vertex AI Pipelines, SageMaker MLOps).
  6. Proficiency in implementing observability stacks (e.g., Prometheus, Grafana, Jaeger, OpenTelemetry, ELK).
  7. Advanced scripting skills (Python, Bash, Go).
  8. Strong understanding of cloud security best practices and automation (e.g., static application security testing (SAST), dynamic application security testing (DAST)).
  9. Experience with GitOps workflows and cost optimization strategies for cloud-based AI/ML workloads.
Nice to Have
  1. Experience with GPU cluster management and scheduling (e.g., Slurm, NVIDIA AI Enterprise, Multi-GPU (MIG)).
  2. Infrastructure for federated learning or edge AI.
  3. Proficiency in policy-as-code frameworks (e.g., Open Policy Agent, Kyverno).
  4. Ai model serving optimization and inference acceleration (e.g., TensorRT, ONNX Runtime).
  5. Experience building and managing serverless infrastructure for AI.

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