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Cloud Engineer – AI Gateway
Softcard (acquired by Google). Design, deploy, and operate secure, scalable AI gateway platforms across cloud and Kubernetes environments .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates advanced knowledge of AI gateway architecture and cloud services, with hands-on experience in deploying and operating scalable platforms in cloud and Kubernetes environments. Proficient in securing AI services and implementing monitoring, logging, and troubleshooting practices to ensure operational excellence.
Highest-signal resume keywords
AI Gateway ArchitectureCloud Services (GCP, Azure, AWS)Kubernetes EnvironmentsInfrastructure as CodeSecurity Controls
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI Model IntegrationPlatform AutomationMonitoring and AlertingTroubleshooting and Root Cause AnalysisCapacity PlanningPython ProgrammingScripting Languages (Linux Shell, PowerShell)Declarative ConfigurationPolicy-Based ControlsCost Optimization
Soft Skills
Effective CommunicationCollaborationMentoring
Industry Keywords
Generative AIContent SafetyData ProtectionResponsible AI GovernanceOperational Runbooks
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformKubernetesLinuxPython
About the role
Key responsibilities & impact- Design, deploy, and operate secure, scalable AI gateway platforms across cloud and Kubernetes environments
- Integrate approved AI models, agents, and services through standardized gateway interfaces and reusable patterns
- Automate platform provisioning, configuration, policy deployment, and release processes
- Implement model routing, failover, rate limits, token controls, and caching
- Secure AI traffic using identity, access, encryption, credential protection, private connectivity, and policy-based controls
- Apply prompt and response guardrails, content-safety policies, data protection, and responsible AI controls
- Establish monitoring and alerting for requests, tokens, latency, errors, model usage, costs, and policy enforcement
- Troubleshoot platform, network, provider, and policy issues and lead root cause analysis for production incidents
- Manage platform capacity, availability, upgrades, resiliency testing, backups, and disaster recovery
- Define onboarding standards, reference architectures, service-level objectives, and operational runbooks
- Partner with security, cloud, data, architecture, and application teams to enable compliant AI adoption
- Evaluate emerging AI gateway capabilities and recommend adoption based on security, scalability, supportability, and cost
- Manage project priorities, deliverables, operational commitments, and continuous improvement initiatives
- Mentor engineers and promote consistent platform engineering, security, and governance practices
- Perform other duties as assigned
Requirements
What you’ll need- Typically requires a bachelor's degree and five (5) to eight (8) years of related experience or an equivalent combination
- Advanced knowledge of AI gateway architecture and cloud services across Google Cloud Platform, Microsoft Azure, or Amazon Web Services
- Hands-on experience deploying and operating highly available platform services in cloud and Kubernetes environments
- Experience integrating generative AI models, agents, and services through standardized gateway interfaces and reusable patterns
- Proficiency with infrastructure as code, declarative configuration, automated testing, and deployment pipelines
- Knowledge of model routing, load balancing, failover, rate limiting, token controls, caching, and usage-based cost optimization
- Experience securing AI services through authentication, authorization, encryption, secrets management, private connectivity, and policy-based controls
- Knowledge of prompt and response guardrails, content safety, data protection, auditability, and responsible AI governance
- Proficiency with monitoring, logging, tracing, alerting, usage metering, and cost analysis for distributed AI workloads
- Strong troubleshooting and root cause analysis skills across platform, network, provider, policy, and application integration layers
- Knowledge of capacity planning, service-level objectives, resiliency testing, upgrades, backup validation, and disaster recovery
- Ability to define reference architectures, onboarding standards, operational runbooks, and scalable platform engineering practices
- Ability to communicate and collaborate effectively with security, cloud, data, architecture, application, and business teams
- Experience with Python and one or more scripting languages, such as Linux shell or PowerShell, for platform automation and integration
- Ability to work independently, manage competing priorities, evaluate emerging capabilities, and mentor engineers
Benefits
Comp & perks- Healthcare coverage options
- 401(k)
- Tuition reimbursement
- Vacation pay
- Sick pay
- Holiday pay
- Hybrid work arrangement