Description

You will design and maintain automated MLOps pipelines on Google Cloud Platform to streamline model training, validation, and deployment.

This role is on-site.

Responsibilities

• Build and manage automated data ingestion, transformation, and validation pipelines using Kubeflow Pipelines and Vertex AI Pipelines.
• Containerize feature engineering logic and implement data validation processes, leveraging AI Agents and the Generative Language API to detect and remediate quality issues.
• Set up continuous training (CT) pipelines with Vertex AI Schedules and Cloud Scheduler, including experiment tracking and hyperparameter tuning.
• Manage model versioning and storage in Cloud Storage, containerize models with Docker, and push images to Artifact Registry.
• Build CI/CD workflows for ML models and configure low-latency production serving environments using Vertex AI Endpoints.

Required Skills

• 5+ years of experience with Google Cloud Platform (GCP), specifically Vertex AI, Kubeflow, Cloud Storage, and Artifact Registry.
• Strong proficiency in Python and Java.
• Hands-on experience with containerization using Docker and managing images in Artifact Registry.
• Proven ability to design end-to-end ML pipelines for data management, training, and deployment.
• Experience with CI/CD practices and pipeline automation.
• Familiarity with TensorFlow and experience with experiment tracking and hyperparameter tuning.
• Bachelor's degree in Computer Science or related field.

Preferred Skills

• Experience with the Generative Language API (Gemini model) or other AI Agent integrations.

Key Skills

Education

Bachelor's degree