GCP Distributed Systems Architect

• Excellent communication and stakeholder-management skills, with the ability to influence and guide senior engineering teams
• Proven experience providing technical leadership and architectural guidance across multiple teams
• Strong understanding of the end-to-end machine learning lifecycle, including feature engineering, model training, evaluation, deployment, monitoring, and governance
• Hands-on software engineering experience with strong proficiency in Python
• Experience with Kubeflow Pipelines, Directed Acyclic Graphs (DAGs), and BigQuery
• Advanced expertise in Kubernetes and cloud-native platform architectures
• Proven experience architecting, designing, and leading complex distributed systems in production environments
• Experience designing and supporting highly scalable environments, including clusters operating at 10,000+ pods
• Deep knowledge of Domain-Driven Design (DDD), distributed systems patterns, and modern software architecture principles
• Strong hands-on technical ability to create prototypes, proofs of concept, and reference implementations
• Demonstrated experience performing architecture reviews and code reviews focused on quality, scalability, performance, and maintainability
• Strong expertise with Google Cloud Platform (GCP) and modern cloud architecture patterns
• Experience defining architectural standards, design patterns, and engineering best practices
• Strong understanding of system resiliency, observability, reliability, and operational readiness considerations
• Ability to evaluate trade-offs, challenge architectural decisions, and provide practical recommendations to engineering teams

Nice to have:

• Hands-on experience architecting and deploying production-grade ML solutions using Vertex AI, including Vertex AI Pipelines, Custom Training, Model Registry, Prediction Endpoints, and Model Monitoring
• Experience with core GCP services, including GKE, Pub/Sub, Cloud Storage, IAM, and Dataflow
• Hands-on experience with Apache Beam and large-scale data-processing frameworks
• Experience defining engineering standards, architectural frameworks, and governance models
• Background in platform modernization, cloud transformation, or large-scale data-processing platforms
• Familiarity with event-driven architectures, streaming systems, and real-time data processing
• Experience in Architect, Principal Engineer, Staff Engineer, Distinguished Engineer, or similar technical leadership roles
• Experience working within consulting, advisory, or architecture-focused organizations

• Define, review, and validate architecture for large-scale distributed systems running on GCP
• Provide technical leadership for solutions built with Kubernetes, Apache Beam, and machine learning platforms
• Conduct architecture and code reviews to ensure scalability, maintainability, performance, security, and adherence to engineering best practices
• Develop proofs of concept (POCs), reference implementations, and sample code to validate architectural patterns and technical approaches
• Guide engineering teams on distributed systems design, data-processing architectures, and Domain-Driven Design (DDD) principles
• Advise on application architecture, service boundaries, scalability, resiliency, observability, and operational readiness
• Support architectural decisions for highly scaled environments, including systems operating at 10,000+ Kubernetes pods
• Partner with engineering and leadership teams to establish architectural standards, governance, and engineering best practices
• Collaborate closely with engineering teams as a hands-on technical advisor while remaining outside day-to-day DevOps and operational ownership