GCP ML Engineer (Vertex AI)

at  Cloud MR

We are seeking a GCP ML Engineer with expertise in Vertex AI to join our team on a contract basis. The successful candidate will be responsible for designing, building, and deploying machine learning models on the Google Cloud Platform (GCP) using Vertex AI. The candidate should have strong experience in Python programming and experience with TensorFlow or PyTorch.

Responsibilities:

• Design and build machine learning models using Vertex AI on GCP
• Collaborate with cross-functional teams to gather requirements and deliver solutions
• Develop and optimize data pipelines for machine learning models
• Implement model monitoring and evaluation techniques to ensure model accuracy and performance
• Work with data engineers to design and implement data architectures to support machine learning workflows
• Ensure adherence to coding standards, testing procedures, and deployment processes
• Continuously research and evaluate new technologies and machine learning techniques to improve model performance and efficiency

Qualifications:

• 3+ years of experience in machine learning engineering
• Strong experience with GCP and Vertex AI
• Experience with Python programming and machine learning libraries such as TensorFlow or PyTorch
• Experience with data processing tools such as Apache Beam or Apache Airflow
• Experience with containerization technologies such as Docker and Kubernetes

This is a contract position with the potential for extension or conversion to full-time employment. If you are passionate about machine learning and have a strong background in GCP and Vertex AI, we encourage you to apply for this exciting opportunity

• Design and build machine learning models using Vertex AI on GCP
• Collaborate with cross-functional teams to gather requirements and deliver solutions
• Develop and optimize data pipelines for machine learning models
• Implement model monitoring and evaluation techniques to ensure model accuracy and performance
• Work with data engineers to design and implement data architectures to support machine learning workflows
• Ensure adherence to coding standards, testing procedures, and deployment processes
• Continuously research and evaluate new technologies and machine learning techniques to improve model performance and efficienc