Google Cloud Platform Architect/Developer

• Architect, build, deploy, and operate production cloud-native workloads using core Google Cloud Platform services
• Design and implement CI/CD pipelines, infrastructure automation, and deployment strategies
• Use AI-assisted developer tools and agents to improve engineering productivity, code quality, and knowledge acquisition

Technologies:

• AI
• AI Agents
• API
• Architect
• CI/CD
• Claude Code
• Cloud
• Copilot
• DevOps
• GCP
• Golang
• Kubernetes
• Machine Learning
• Mobile
• Python
• RBAC
• Security

• Formal training or certification in software engineering concepts and 5+ years of applied experience in production software development
• Advanced hands-on experience delivering system design, application development, testing, and operational stability at scale
• Advanced proficiency in one or more modern programming languages, with the ability to optimize performance, maintainability, and security; GoLang or Python is preferred, including experience with performance optimization and concurrency patterns
• Advanced Kubernetes platform engineering expertise, including hands-on experience designing, developing, and operating custom Kubernetes Operators and Custom Resource Definitions (CRDs) using Kubebuilder, Operator SDK, or equivalent
• Experience extending Kubernetes APIs to automate complex platform, infrastructure, security, or application lifecycle workflows at enterprise scale
• Experience building cloud-native control planes and automation frameworks on Kubernetes, including reconciliation loops, admission controllers, webhooks, and custom controllers
• Strong understanding of Kubernetes internals, API machinery, RBAC, multi-tenancy, observability, and production operational best practices
• In-depth Google Cloud development experience, including understanding of cost optimization and security best practices
• Proficiency in automation and continuous delivery
• Advanced understanding of the Software Development Life Cycle and the ability to influence and improve SDLC practices across teams
• Advanced knowledge of agile methodologies, CI/CD, application resiliency, security, and capacity planning
• Demonstrated expertise in a technical discipline such as cloud architecture, artificial intelligence/machine learning, mobile, distributed systems, security, or DevOps/platform engineering
• In-depth knowledge of the financial services industry, its IT systems, regulatory constraints, and operational requirements
• Practical experience with AI-assisted developer tools and AI agents such as Copilot, Claude Code, or equivalent
• Strong communication and influence skills, including the ability to articulate technical decisions, trade-offs, and recommendations to technical and non-technical stakeholders