AI Platform Engineer/DevOp

We are seeking a senior AI Platform Engineer to build, operate, and scale the infrastructure and tooling that power AI/ML and generative AI workloads. This role focuses on cloud platform engineering, CI/CD automation, and MLOps practices that enable reliable, secure, and scalable delivery of AI systems. Key Responsibilities Design, build, and maintain scalable cloud infrastructure on GCP to support AI/ML and application workloads Architect and manage CI/CD pipelines for model training, deployment, and application release workflows (Cloud Build, GitHub Actions, Jenkins, GitLab CI, or similar) Build and maintain MLOps pipelines for model versioning, training, deployment, and monitoring (Vertex AI Pipelines, Kubeflow, MLflow) Implement Infrastructure as Code (Terraform, Deployment Manager) for repeatable, auditable environment provisioning Containerize and orchestrate services using Docker and Kubernetes (GKE) Establish observability, logging, and alerting for AI/ML services (Cloud Monitoring, Cloud Logging, Prometheus/Grafana) Partner with AI/ML engineers and data scientists to productionize models and streamline the path from experimentation to production Ensure platform security, IAM policies, secrets management, and compliance across environments Drive automation to reduce manual toil across build, test, deployment, and rollback processes Troubleshoot platform/infrastructure issues and drive root-cause resolution for production incidents

7 years of experience in platform engineering, DevOps, or infrastructure engineering roles Strong hands-on expertise with Google Cloud Platform (GCP) - Vertex AI, GKE, Cloud Run, IAM, VPC/networking, BigQuery Deep experience designing and managing CI/CD pipelines (Cloud Build, Jenkins, GitHub Actions, GitLab CI, ArgoCD) Proficiency in Infrastructure as Code (Terraform preferred) Strong scripting/programming skills (Python, Bash, or Go) Experience with containerization and orchestration (Docker, Kubernetes) Solid understanding of MLOps concepts and the ML lifecycle (training, versioning, deployment, monitoring) Experience with observability tooling (Cloud Monitoring, Prometheus, Grafana, ELK/EFK) Strong understanding of cloud security, IAM, and secrets management best practices Preferred Qualifications GCP Professional certifications (Cloud DevOps Engineer, Cloud Architect, or Machine Learning Engineer) Experience supporting generative AI / LLM platforms (Vertex AI, Gemini Enterprise, Model Garden) Experience with GitOps workflows (ArgoCD, Flux) Background working in regulated or enterprise-scale environments Familiarity with cost optimization and FinOps practices on GCP