Net2Source (N2S)Remote
Sr AI Engineer -(GCP and Vertex AI)
United States
Full-time
3 days ago
H1B Sponsor Likely
Key skills
Vertex AI MLOpsGoogle Cloud PlatformCI/CD IntegrationTraditional MLSupervised learning modelsInfrastructure as CodeAIMachine LearningMLGenerative AIMLOpsKubeflowBigQueryGCPGoogle CloudTerraformDockerCloud StorageCloud BuildVertex AIGitHubCI/CD
About this role
Net2Source (N2S) is seeking a Senior AI Engineer with expertise in Google Cloud Platform and Vertex AI. The role focuses on utilizing Vertex AI for predictive and generative AI, establishing MLOps practices, and automating ML workflows.
Responsibilities:
- Cloud Platform & AI Services (Google Cloud/Vertex AI) - The core of the project relies on Google Cloud Platform (GCP) and specifically Vertex AI. Looking for Proficient in:
- Vertex AI Capabilities: Using built-in features for predictive and generative AI, as well as the Model Garden to discover and customize models
- Vertex AI Pipelines: Building and executing steps in Vertex Pipelines, including understanding how to use Kubeflow to build pipeline templates for interoperability
- Core GCP Services: Configuring and enabling APIs for BigQuery, Google Cloud Storage, and managed Vertex AI notebooks
- MLOps & Automation - A major focus of the engagement is establishing Machine Learning Operations (MLOps) maturity. Required skills include:
- CI/CD Integration: Implementing Continuous Integration/Continuous Delivery workflows using Cloud Build or GitHub to deploy pipeline components
- Model Lifecycle Management: Managing models through the Vertex AI Model Registry, using Artifact Registry for Docker images, and setting up Vertex AI Experiments for tracking pipeline runs
- Infrastructure as Code (IaC): Developing automation scripts (likely Terraform, though ??IaC?? is the term used) to manage the lifecycle of AI/ML sandbox projects and environments
Requirements:
- Proficient in Google Cloud Platform (GCP) and specifically Vertex AI
- Using built-in features for predictive and generative AI, as well as the Model Garden to discover and customize models
- Building and executing steps in Vertex Pipelines, including understanding how to use Kubeflow to build pipeline templates for interoperability
- Configuring and enabling APIs for BigQuery, Google Cloud Storage, and managed Vertex AI notebooks
- Establishing Machine Learning Operations (MLOps) maturity
- Implementing Continuous Integration/Continuous Delivery workflows using Cloud Build or GitHub to deploy pipeline components
- Managing models through the Vertex AI Model Registry, using Artifact Registry for Docker images, and setting up Vertex AI Experiments for tracking pipeline runs
- Developing automation scripts (likely Terraform) to manage the lifecycle of AI/ML sandbox projects and environments