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About the Role

Drive the design, deployment, and operationalisation of machine learning solutions on Google Cloud, bridging AI/ML engineering and cloud delivery to ensure reliable, secure, and scalable production models and pipelines.

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

• 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.
• 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.

Skills

• Vertex AI
• MLOps
• Kubeflow Pipelines
• Vertex AI Model Registry
• Vertex AI Endpoints
• Vertex AI Batch Prediction
• Vertex AI Feature Store
• BigQuery
• Dataflow
• Cloud Storage
• Pub/Sub
• Terraform
• GCP IAM
• VPC Service Controls
• Docker
• GKE
• Python
• TensorFlow
• PyTorch
• Scikit-learn
• LLM fine-tuning
• Vertex AI Generative AI Studio
• Model Garden
• Feast
• Tecton
• Ansible
• DataOps
• Platform Engineering

Experience Level

• 4+ years of experience with GCP
• 2+ years hands-on with Vertex AI