Lead ML Architect – Google Cloud

This is a fully remote position, open to applicants in Poland.

📋 Description

• Take responsibility for the target architecture of enterprise ML and MLOps platforms on Google Cloud.

• Facilitate architecture workshops, discovery sessions, and technical discussions with enterprise clients.

• Convert business, ML, data, security, and operational needs into scalable architecture designs.

• Create comprehensive Machine Learning lifecycle patterns that encompass data preparation, feature engineering, experimentation, model training, evaluation, model registration, and promotion.

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

• Develop reusable and modular pipeline architectures that can support multiple teams and ML workloads.

• Define governance frameworks that include approval processes, lifecycle policies, lineage tracking, and standards for reproducibility.

• Create feature management solutions utilizing BigQuery and Google Cloud-native services.

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

• Determine monitoring strategies that encompass model performance, drift detection, observability, and ground-truth validation.

• Offer architectural guidance to MLOps, Cloud, and Data Engineering teams during implementation.

• Drive technical decision-making while ensuring alignment with enterprise architecture standards.

• Maintain architecture documentation, technology roadmaps, and implementation guidelines.

• Advocate for engineering excellence and the adoption of cloud-native Machine Learning best practices throughout the organization.

⛳️ Requirements

• Demonstrated experience in designing and delivering production-grade ML and MLOps platforms within enterprise environments.

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

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

• Proven capability in designing end-to-end ML lifecycle architectures and pipeline orchestration frameworks.

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

• Extensive knowledge of BigQuery and its significance in enterprise-scale Machine Learning ecosystems.

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

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

• Experience in developing solutions that function across development, testing, and production environments.

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

• Solid software engineering background with an architectural mindset.

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

• Experience collaborating directly with enterprise clients and senior technical stakeholders.

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

• Capability to navigate comfortably between strategic architecture planning and implementation-level engineering details.

• Fluent English (C1).

• Nice to have: Google Cloud Professional Cloud Architect certification.

• Nice to have: Google Cloud Professional Machine Learning Engineer certification.

• Nice to have: Experience with AI and Generative AI platforms deployed in enterprise environments.

• Nice to have: Knowledge of Responsible AI, model governance, and enterprise AI adoption frameworks.

• Nice to have: Experience utilizing AI tools in daily workflows.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health and wellness benefits.

• Opportunities for professional development and certification.

• Flexible working hours and remote work options.

• Collaborative and innovative work environment.