Lead ML Architect (Google Cloud)

Spyrosoft is a software engineering company established in 2016, specializing in technology solutions for industry 4.0, automotive, geospatial, healthcare & life sciences, employee experience & education, and financial services industries.

• 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 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

• 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