DevOps Engineer

Key Responsibilities: Design, provision, and maintain GCP infrastructure across development, testing, and production environments. Build and manage CI/CD pipelines using Cloud Build, Cloud Deploy, Jenkins, GitHub Actions, or similar tools. Automate infrastructure provisioning and configuration using Terraform and reusable infrastructure modules. Deploy and operate containerized applications using Docker, Google Kubernetes Engine (GKE), and Cloud Run. Configure networking, IAM, service accounts, secrets management, and security controls. Implement monitoring, logging, alerting, and dashboards using Cloud Monitoring and Cloud Logging. Troubleshoot infrastructure, deployment, networking, and application performance issues. Support availability, backup and recovery, incident response, and cloud cost optimization. Collaborate with application and data teams to deploy and support AI-enabled services, including applications that consume model APIs. Assist with Vertex AI integrations, deployment automation, and monitoring for AI workloads. Apply appropriate access controls and data protection practices to AI services and supporting infrastructure. Maintain operational documentation, runbooks, and deployment standards.

Required Qualifications: 8+ years of experience in DevOps domain. Strong hands-on experience in DevOps, cloud infrastructure, or site reliability engineering. Practical experience with GCP services including Compute Engine, GKE, Cloud Run, Cloud Storage, IAM, and VPC networking. Experience building and maintaining CI/CD pipelines for enterprise applications. Proficiency with Terraform and infrastructure as code practices. Strong knowledge of Docker, Kubernetes, Linux administration, and troubleshooting. Scripting skills in Python, Bash, or a similar language. Experience with cloud monitoring, centralized logging, alerting, and incident resolution. Understanding of cloud security, least-privilege access, secrets management, and network controls. Some hands-on exposure to AI or ML workloads through deployment support, integrations, or proof-of-concept work. Familiarity with Vertex AI or comparable cloud AI services and the operational needs of AI-powered applications. Strong communication, problem-solving, and cross-functional collaboration skills.

Preferred Qualifications: Google Cloud Professional Cloud DevOps Engineer or Professional Cloud Architect certification. Experience with Helm, Argo CD, or GitOps deployment practices. Familiarity with Artifact Registry, Secret Manager, Pub/Sub, BigQuery, and Cloud SQL.Exposure to MLOps concepts, model versioning, model deployment, and pipeline automation. Basic understanding of Generative AI, LLM APIs, embeddings, or retrieval-augmented generation (RAG).Experience monitoring AI service latency, usage, quotas, and cost. Familiarity with vulnerability scanning, policy enforcement, and automated security checks. Experience supporting Java or Python applications on GCP.