Description

You build, deploy, and maintain scalable machine learning systems across their full lifecycle.

Responsibilities

• Design and manage automated data ingestion, transformation, and validation pipelines using Kubeflow Pipelines and Vertex AI Pipelines.
• Implement and containerize feature engineering logic for reusable and scalable datasets.
• Integrate data validation processes, leveraging AI Agents or Generative Language API to detect and remediate quality issues.
• Establish model versioning, containerize models with Docker, and build CI/CD workflows for automated deployment.
• Configure and manage low-latency production serving environments using Vertex AI Endpoints for real-time inference.

Required Skills

• 5+ years of professional experience.
• Strong experience with Google Cloud Platform (GCP) services in the MLOps domain (Vertex AI, Kubeflow, Cloud Storage, Artifact Registry).
• Proven ability to design and implement end-to-end ML pipelines for data management, training, and deployment.
• Hands-on experience with containerization technologies, specifically Docker.
• Familiarity with CI/CD practices and pipeline automation.
• Knowledge of ML frameworks like TensorFlow.
• Experience with experiment tracking and hyperparameter tuning.
• Excellent problem-solving skills and understanding of the ML lifecycle.

Key Skills

Education

Any Graduate