Lead ML Architect (Google Cloud)

Join a strategic cloud and AI transformation program focused on building enterprise-scale Machine Learning platforms on Google Cloud. The role involves shaping architecture standards and best practices around Vertex AI and Gemini Enterprise Agent Platform Pipelines, supporting multiple business domains and data products.

• 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 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 ability to facilitate architecture workshops and technical discussions
• Ability to operate between strategic architecture planning and implementation-level engineering details

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

• 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