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