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We are seeking a GCP Distributed Systems Architect with deep expertise in software engineering, solution architecture, Kubernetes, machine learning, data and analytics, and large-scale distributed systems.
This is a highly technical, hands-on architecture role focused on solution design, technical leadership, architecture reviews, code reviews, engineering best practices, and proof-of-concept development. The role is centered on defining and validating architectural approaches rather than owning day-to-day feature development, MLOps, or operational support.
The ideal candidate has extensive experience designing and guiding complex data-processing platforms on Google Cloud Platform (GCP). They should be able to evaluate architectural decisions, assess implementation quality, and provide practical technical direction across engineering teams. The role requires someone who can create POCs and reference implementations when needed while primarily driving design excellence, scalability, and engineering standards.
Working Hours: The role requires regular collaboration with key stakeholders based in the Pacific Time Zone. Candidates should be available for meetings and communication through approximately 1:00–2:00 PM PT on a daily basis.
- 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
- 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
- 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