Machine Learning Engineer - Google Cloud Platform

Overview

Role Overview :

We are seeking a skilled ML Engineer Google Cloud to design, build, and deploy scalable end-to-end machine learning solutions for enterprise clients.

In this role, you will work extensively with Vertex AI to develop, automate, and operationalize ML workflows, ensuring reliable model performance across real-time and batch use cases.

Key Responsibilities

• Design and implement end-to-end machine learning pipelines on Google Cloud Platform (GCP).
• Develop, train, evaluate, and deploy ML models at scale using Vertex AI.
• Build and automate MLOps workflows using Vertex AI Pipelines (Kubeflow).
• Manage model lifecycle including versioning, monitoring, and governance via Vertex AI Model Registry.
• Implement centralized and reusable feature management using Vertex AI Feature Store.
• Enable real-time and batch inference using Vertex AI Prediction endpoints.
• Leverage BigQuery ML for in-database model training and large-scale inference.
• Collaborate with data engineers, cloud architects, and stakeholders to deliver production-ready ML solutions.
• Create clear technical documentation and communicate solutions effectively to clients.
  
  

(68 of the following required)

• Vertex AI Workbench for experimentation and model development
• Vertex AI Pipelines (Kubeflow) for ML workflow orchestration
• Vertex AI Model Registry and model versioning strategies
• Vertex AI Feature Store for centralized feature management
• BigQuery ML for model training and inference within BigQuery
• Vertex AI Prediction for batch and real-time serving
• AutoML (Tabular, Image, Text, Video) on Vertex AI
• Model training using TensorFlow and/or PyTorch on Vertex AI Training
  
  

• 3+ years of overall experience in Machine Learning Engineering
• At least 1 year of hands-on experience deploying ML models on Google Cloud
• Proven track record of delivering 2+ production-grade ML models using Vertex AI or BigQuery ML
  
  

(ref:hirist.tech)