
💼 Key Responsibilities:
Build automated ML pipelines for training, validation, deployment, and monitoring using Vertex AI Pipelines, Kubeflow, or TFX
Leverage Vertex AI Workbench, Training, and Experiments for reproducible, collaborative model development
Deploy, manage, and monitor models using Vertex AI Model Registry, Model Monitoring, and Prediction Endpoints
Collaborate with Data Scientists to productionize notebooks and prototypes into scalable ML services
Monitor model performance, detect data/model drift, and ensure data quality using Vertex AI Monitoring
Containerize and orchestrate ML workloads using Docker and Kubernetes (GKE)
Build and maintain robust CI/CD pipelines using Cloud Build, Jenkins, or Bitbucket Pipelines
Ensure strong version control, security, and compliance across the ML lifecycle
Maintain comprehensive documentation, templates, and artifacts to enable reproducibility, governance, and fast onboarding
🛠Skills & Tools:
Vertex AI (Pipelines, Training, Workbench, Model Registry, Monitoring, Prediction Endpoints)
GCP Core Services: GCS, BigQuery, Pub/Sub, Dataflow
MLOps Tools: Kubeflow, TFX, MLflow (optional)
DevOps Tools: Docker, Kubernetes (GKE), CI/CD (Cloud Build, Jenkins)
Languages: Python, YAML, Shell scripting
Version Control & Tracking: Git, DVC (optional), TensorBoard
✅ Preferred Qualifications:
GCP Professional Machine Learning Engineer certification is a plus
Experience working with regulated environments and secure ML pipelines
Strong understanding of ML lifecycle, MLOps principles, and cloud cost
<p>💼 Key Responsibilities:<br> Build automated ML pipelines for training, validation, deployment, and monitoring using Vertex AI Pipelines, Kubeflow, or TFX<br> Leverage Vertex AI Workbench, Training, and Experiments for reproducible, collaborative model development<br> Deploy, manage, and monitor models using Vertex AI Model Registry, Model Monitoring, and Prediction Endpoints<br> Collaborate with Data Scientists to productionize notebooks and prototypes into scalable ML services<br> Monitor model performance, detect data/model drift, and ensure data quality using Vertex AI Monitoring<br> Containerize and orchestrate ML workloads using Docker and Kubernetes (GKE)<br> Build and maintain robust CI/CD pipelines using Cloud Build, Jenkins, or Bitbucket Pipelines<br> Ensure strong version control, security, and compliance across the ML lifecycle<br> Maintain comprehensive documentation, templates, and artifacts to enable reproducibility, governance, and fast onboarding<br></p> <p>🛠Skills & Tools:<br> Vertex AI (Pipelines, Training, Workbench, Model Registry, Monitoring, Prediction Endpoints)<br> GCP Core Services: GCS, BigQuery, Pub/Sub, Dataflow<br> MLOps Tools: Kubeflow, TFX, MLflow (optional)<br> DevOps Tools: Docker, Kubernetes (GKE), CI/CD (Cloud Build, Jenkins)<br> Languages: Python, YAML, Shell scripting<br> Version Control & Tracking: Git, DVC (optional), TensorBoard<br></p> <p>✅ Preferred Qualifications:<br> GCP Professional Machine Learning Engineer certification is a plus<br> Experience working with regulated environments and secure ML pipelines<br> Strong understanding of ML lifecycle, MLOps principles, and cloud cost<br></p>