Vertex AI Engineer and Cloud Delivery Lead

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
• Drive adoption of GKE for model serving workloads where custom inference infrastructure is required

REQUIRED QUALIFICATIONS:

• 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 containerization (Docker) and GKE for model serving

PREFERRED QUALIFICATIONS

• 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