AI Platform Engineer
Tasks
- Add tool access controls
- Build golden test suites
- Build human review workflows
- Build repository integrations
- Build responsible notification systems
- Build scalable AI workflows
- Create CI gates for AI quality
- Create context distribution systems
- Create data pipelines for AI metrics
- Create event triggered workflows
- Create human approval workflows
- Design AI agent production architecture
- Design AI execution platform
- Develop AI evaluation layer
- Develop compilers for AI workflows
- Develop platform tooling and validators
- Develop rollback mechanisms
- Enforce least privilege permissions
- Implement audit logging
- Implement durable state management
- Implement governance mechanisms
- Implement safety checks
- Instrument AI workflows for usage and cost tracking
- Maintain run logging and tracing
- Measure AI adoption and ROI
- Monitor agent performance and improvements
- Restrict high risk AI actions
Perks/Benefits
- Annual offsite
- Coworking allowance
- Dependent coverage
- Employee equity ownership
- Flexible, asynchronous work
- Fully remote
- Global team collaboration
- Health insurance
- Open source community impact
- Professional development budget
- Remote-first culture
- Technology allowance
Skills/Tech-stack
API Integration | Access Control | Audit Logging | CI/CD | Cloud Computing | Cloud Data | Cloud data warehousing | Cloud platform | Cost Tracking | Data Pipelines | Data Warehousing | Event Driven | Event-driven architecture | Golden datasets | Google Cloud | Google Cloud Platform | Human-in-the-loop | Infrastructure as Code | LLM | LLM Evaluation | Language Models | Large Language Models | Model Observability | Permissions Management | Python | Safety testing | Security | State management | The Loop | Tracing | “as-code”
Education
N/A
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