Lead ML Architect (Google Cloud)

Project description:

Join a strategic cloud and AI transformation program focused on building enterprise-scale Machine Learning platforms on Google Cloud. As a Lead ML Architect, you will define the target architecture for advanced ML and MLOps ecosystems, enabling Data Science, Data Engineering and Cloud teams to develop, deploy and operate Machine Learning solutions efficiently and securely.

You will play a key role in shaping architecture standards and best practices around Vertex AI and Gemini Enterprise Agent Platform Pipelines, supporting multiple business domains and data products. Working closely with enterprise stakeholders, you will translate business and technical requirements into scalable platform solutions and provide architectural leadership throughout implementation.

This role combines customer-facing consulting, architecture ownership and deep technical expertise in modern Machine Learning platforms.

Tech stack:

• Google Cloud Platform (GCP)

• Vertex AI

• Gemini Enterprise Agent Platform Pipelines

• BigQuery

• Cloud Storage, Cloud Composer

• Dataflow

• Kubernetes (GKE), Terraform, CI/CD

• Python

• MLOps

• IAM

• Model Registry

• Monitoring & Observability

Requirements:

• Proven experience designing and delivering production-grade ML and MLOps platforms in enterprise environments

• Deep hands-on expertise with Google Cloud Platform (GCP)

• Strong production experience with Vertex AI and Gemini Enterprise Agent Platform Pipelines

• Proven ability to design end-to-end ML lifecycle architectures and pipeline orchestration frameworks

• Strong understanding of modular, reusable and scalable ML pipeline design patterns

• Extensive knowledge of BigQuery and its role within enterprise-scale Machine Learning ecosystems

• Practical experience with ML lifecycle management, model monitoring, retraining strategies, rollback mechanisms and reproducibility standards

• Hands-on experience designing CI/CD and deployment patterns for Machine Learning solutions

• Experience building solutions that operate across development, testing and production environments

• Strong understanding of cloud security, IAM, governance and compliance principles

• Solid software engineering background with architectural mindset

• Ability to evaluate architecture trade-offs related to scalability, security, maintainability, operability and cost optimization

• Experience working directly with enterprise customers and senior technical stakeholders

• Excellent communication skills and the ability to facilitate architecture workshops and technical discussions

• Ability to operate comfortably between strategic architecture planning and implementation-level engineering details

• Fluent English (C1)

Nice to have:

• Google Cloud Professional Cloud Architect certification

• Google Cloud Professional Machine Learning Engineer certification

• Experience with AI and Generative AI platforms deployed in enterprise environments

• Knowledge of Responsible AI, model governance and enterprise AI adoption frameworks

• Experienced in using AI tools in day-to-day workflow

Main responsibilities:

• Own the target architecture of enterprise ML and MLOps platforms on Google Cloud

• Lead architecture workshops, discovery sessions and technical discussions with enterprise customers

• Translate business, ML, data, security and operational requirements into scalable architecture designs

• Design end-to-end Machine Learning lifecycle patterns covering data preparation, feature engineering, experimentation, model training, evaluation, model registration and promotion

• Define architecture standards and best practices for Vertex AI and Gemini Enterprise Agent Platform Pipelines

• Design reusable and modular pipeline architectures supporting multiple teams and ML workloads

• Define governance frameworks including approval processes, lifecycle policies, lineage tracking and reproducibility standards

• Design feature management solutions leveraging BigQuery and Google Cloud-native services

• Establish integration patterns between ML platforms, enterprise data ecosystems, CI/CD frameworks, IAM and governance solutions

• Define monitoring strategies covering model performance, drift detection, observability and ground-truth validation

• Provide architectural guidance to MLOps, Cloud and Data Engineering teams throughout implementation

• Drive technical decision-making and ensure alignment with enterprise architecture standards

• Maintain architecture documentation, technology roadmaps and implementation guidelines

• Promote engineering excellence and adoption of cloud-native Machine Learning best practices across the organization