Vertex AI Engineer and Cloud Delivery Lead

Appealing Points:

• Lead End-to-End MLOps on Vertex AI – Design and implement scalable ML pipelines covering data ingestion, training, deployment, monitoring, and model lifecycle management on Google Cloud.
• Build Enterprise-Grade AI Platforms – Work with Vertex AI, BigQuery, Dataflow, GCS, and Pub/Sub while enabling reliable model serving using GKE and automated CI/CD workflows.
• Drive Production AI Delivery & Governance – Collaborate with data scientists and engineering teams to operationalize ML models, enforce IAM/security controls, and ensure robust, production-ready AI systems.

Annual salary: 10 million and above

Job 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

Job Qualification:

• 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 Skills:

• 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

Language Skills: Business level Japanese (JLPT N2 and above) and Business level English

Company Description:

One of the world's leading professional services companies, transforming clients' business, operating and technology models for the digital era.

Their unique industry-based, consultative approach helps clients envision, build and run more innovative and efficient businesses.

Headquartered in the U.S., this company is one of the Fortune 500 companies and is consistently listed among the most admired companies in the world.

[Passive smoking measures]

Indoor smoking

Designated smoking area

. Skillset Required: MLOps, Vertex AI, ML pipelines, data ingestion, model training, model deployment, model monitoring, model lifecycle management, Google Cloud, BigQuery, Dataflow, GCS, Pub/Sub, GKE, CI/CD workflows, IAM, security controls, Python, TensorFlow, PyTorch, Scikit-learn, Vertex AI Pipelines, Vertex AI Model Registry, Kubernetes, Batch Prediction, feature engineering, Vertex AI Feature Store, Terraform, service account governance, VPC Service Controls, sprint planning, release management, environment promotion, stakeholder communication, data drift detection, data skew detection, containerisation, Docker, Google Professional Machine Learning Engineer certification, LLM fine-tuning, Vertex AI Generative AI Studio, Model Garden, Feast, Tecton, Ansible, DataOps, platform engineering