GCP Vertex AI Engineer

Role Overview

We are seeking an experienced Vertex AI Engineer and Cloud Delivery Lead to drive the design deployment and operationalization of machine learning solutions on Google Cloud. This role bridges AI/ML engineering and cloud delivery ensuring that models and pipelines reach production reliably securely and at scale.

Key Responsibilities

• Design and implement end-to-end MLOps pipelines on Vertex AI including data ingestion model training evaluation and deployment.
  
• Build and manage Vertex AI Pipelines (Kubeflow Pipelines) for automated model training and retraining workflows.
  
• Deploy and manage models using Vertex AI Model Registry Endpoints and Batch Prediction services.
  
• Implement feature engineering workflows using Vertex AI Feature Store.
  
• Develop GCP-native integrations connecting Vertex AI with BigQuery Dataflow Cloud Storage and Pub/Sub
  
• Manage infrastructure for ML workloads using Terraform ensuring reproducible and version-controlled environments.
  
• Configure IAM policies for Vertex AI workloads including service account governance and VPC Service Controls.
  
• Lead cloud delivery activities: sprint planning release management environment promotion and stakeholder communication.
  
• Establish model monitoring using Vertex AI Model Monitoring for data drift and skew detection.
  
• Collaborate with data scientists to containerise experiments and promote models through dev/staging/production.
  

Requirements

Skills and Experience

• 4 years of experience with GCP including 2 years hands-on with Vertex AI.
  
• Strong proficiency in Python and ML frameworks (TensorFlow PyTorch Scikit-learn).
  
• Experience building Vertex AI Pipelines and managing model lifecycle in Vertex AI Model Registry.
  
• Solid Terraform skills for provisioning Vertex AI GCS BigQuery and associated infrastructure.
  
• Good understanding of GCP IAM particularly for securing ML pipelines and data access.
  
• Experience with GCP-native development patterns and event-driven architectures.
  
• Demonstrated cloud delivery experience including planning execution and stakeholder management.
  
• Familiarity with containerisation (Docker) and GKE for model serving.
  

Required Certifications

• Google Professional Machine Learning Engineer certification.
  
• Experience with LLM fine-tuning Vertex AI Generative AI Studio or Model Garden.
  
• Familiarity with Feast Tecton or similar feature stores.
  
• Experience with Ansible for environment configuration and automation.
  
• Background in DataOps or platform engineering for data-intensive workloads.
  

Core Skills & Technologies

• Vertex AI GCP Native Dev Terraform GKE
  
• GCP IAM BigQuery Kubeflow Pipelines Python / ML Frameworks
  
• Cloud Storage Model Monitoring Ansible MLOps
  
  

Required Skills:

Role Overview

We are seeking an experienced Vertex AI Engineer and Cloud Delivery Lead to drive the design deployment and operationalization of machine learning solutions on Google Cloud. This role bridges AI/ML engineering and cloud delivery ensuring that models and pipelines reach production reliably securely and at scale.

Key Responsibilities

• Design and implement end-to-end MLOps pipelines on Vertex AI including data ingestion model training evaluation and deployment.
  
• Build and manage Vertex AI Pipelines (Kubeflow Pipelines) for automated model training and retraining workflows.
  
• Deploy and manage models using Vertex AI Model Registry Endpoints and Batch Prediction services.
  
• Implement feature engineering workflows using Vertex AI Feature Store.
  
• Develop GCP-native integrations connecting Vertex AI with BigQuery Dataflow Cloud Storage and Pub/Sub
  
• Manage infrastructure for ML workloads using Terraform ensuring reproducible and version-controlled environments.
  
• Configure IAM policies for Vertex AI workloads including service account governance and VPC Service Controls.
  
• Lead cloud delivery activities: sprint planning release management environment promotion and stakeholder communication.
  
• Establish model monitoring using Vertex AI Model Monitoring for data drift and skew detection.
  
• Collaborate with data scientists to containerise experiments and promote models through dev/staging/production.
  

Requirements

Skills and Experience

• 4 years of experience with GCP including 2 years hands-on with Vertex AI.
  
• Strong proficiency in Python and ML frameworks (TensorFlow PyTorch Scikit-learn).
  
• Experience building Vertex AI Pipelines and managing model lifecycle in Vertex AI Model Registry.
  
• Solid Terraform skills for provisioning Vertex AI GCS BigQuery and associated infrastructure.
  
• Good understanding of GCP IAM particularly for securing ML pipelines and data access.
  
• Experience with GCP-native development patterns and event-driven architectures.
  
• Demonstrated cloud delivery experience including planning execution and stakeholder management.
  
• Familiarity with containerisation (Docker) and GKE for model serving.
  

Required Certifications

• Google Professional Machine Learning Engineer certification.
  
• Experience with LLM fine-tuning Vertex AI Generative AI Studio or Model Garden.
  
• Familiarity with Feast Tecton or similar feature stores.
  
• Experience with Ansible for environment configuration and automation.
  
• Background in DataOps or platform engineering for data-intensive workloads.
  

Core Skills & Technologies

• Vertex AI GCP Native Dev Terraform GKE
  
• GCP IAM BigQuery Kubeflow Pipelines Python / ML Frameworks
  
• Cloud Storage Model Monitoring Ansible MLOps
  
  

Employment Type : Full Time
Experience: years
Vacancy: 1