Summary: The primary purpose of this role is to take charge of deploying, operating, and supporting AI/GenAI workloads in a production workplace, focusing on utilizing Microsoft Azure and various DevOps practices to ensure operational reliability and efficiency.
Main Responsibilities
- Independently deploy, operate, and support AI/GenAI workloads in production environments.
- Manage Azure resources, including identity, security, and networking.
- Administer and manage Azure Kubernetes Service (AKS), including all related activities.
- Implement Kubernetes networking and traffic management strategies.
- Implement autoscaling and event-driven scaling solutions.
- Design and implement CI/CD pipelines with a focus on security and integration.
- Conduct monitoring and observability implementations for workloads.
- Establish FinOps practices for cost management and optimization.
- Troubleshoot production issues effectively and independently.
- Own production deployments and ensure operational reliability.
Key Requirements
- 7-12 years of experience in DevOps or Cloud Engineering.
- Hands-on experience deploying and managing AI workloads.
- Strong expertise in Microsoft Azure and related services.
- Experience with Docker and Azure Kubernetes Service (AKS).
- Proficient in CI/CD pipeline design and implementation.
- Familiarity with monitoring and observability tools.
- Skilled in troubleshooting complex production issues.
Nice to Have
- Experience with FinOps practices.
- Proficiency in AI/LLM observability and evaluation.
- Knowledge of DevSecOps security practices.
- Proficiency in both English and French.
- Knowledge of enterprise-scale project management.