We are seeking an experienced Vertex AI Engineer and Cloud Delivery Lead to drive the design, deployment, and operationalisation 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.
Job Description - Grade Specific
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
Requirements
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 containerisation (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
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