Description
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.
Technical Skills Required
(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
Experience & Qualifications
- 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)
