Senior DevOps Engineer, AI4ALL

Responsibilities

• Deploy and maintain secure cloud infrastructure primarily on GCP (Google Cloud Platform) and AWS, ensuring seamless integration between services.
• Manage and optimize GKE (Google Kubernetes Engine) clusters for high-availability AI applications and microservices.
• Infrastructure as Code: Build and enforce Terraform strategies to provision and manage infrastructure, ensuring environments are reproducible and version-controlled.
• CI/CD & Software Verification: Design and implement advanced CI/CD workflows using GitHub Actions, moving beyond simple deployments to create intelligent automation.
• Build robust verification pipelines that include automated testing, linting, security scanning, and quality gates before production release.
• Streamline the release process for backend and frontend applications, ensuring "one-click" reliability.
• Oversee the deployment, maintenance, and backup strategies for databases.
• Implement comprehensive monitoring and logging solutions (Prometheus, Grafana, Cloud Ops, Cloud Monitoring) to ensure system health, performance, and rapid incident response.
• Implement security best practices (IAM, VPC configuration, encryption) to protect sensitive AI data and intellectual property.

Qualifications Education & Experience

• B.Sc. or M.Sc. in Computer Science, Computer Engineering, or a related technical field.
• 5+ years of relevant experience in DevOps, Cloud Engineering, or Site Reliability Engineering (SRE).
• Proven experience acting as a Senior or Lead engineer, guiding architectural decisions.

Technical Requirements

• Advanced hands-on experience with GCP (specifically GKE, VPC, IAM) and a working knowledge of AWS.
• Proven ability to design and implement robust, scalable, and secure cloud architectures.
• Mastery of Docker and Kubernetes administration.
• Strong proficiency in Terraform.
• Expert knowledge of GitHub Actions for building, test, build, and deploy pipelines.
• Experience managing PostgreSQL and ClickHouse databases.
• Deep understanding of Linux System Administration.
• Experience with AI/ML lifecycle tools (e.g., Kubeflow, MLflow, Vertex AI).
• Strong proficiency in Python and Bash scripting.
• Familiarity with DevSecOps tools and practices.
• Bonus: Official GCP Certifications (e.g., Professional Cloud Architect, Professional Cloud DevOps Engineer) and Kubernetes Certifications (e.g., CKA, CKAD, CKS) are highly preferred.
• Bonus: Knowledge of Javascript/Node.js is a strong plus.

Soft Skills & Mindset

• Ability to design long-term solutions rather than quick fixes.
• Excellent ability to explain complex cloud concepts to Data Scientists and business stakeholders.
• Comfortable working in a fast-paced environment with evolving requirements.
• Fluent English is a must.