Cloud Architect, Product Management

EducationPals; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

EducationPals is seeking a hands-on Cloud Architect to serve as the technical bridge between Engineering, Product Management, and Field teams. You will be the primary SME on the cloud services we support, ensuring our product vision translates into sound, commercially viable solutions.

This role balances technical execution with product strategy, guiding architecture decisions, security controls, and scalable deployments across the platform.

Responsibilities

• Build authorization systems that control what AI agents can access and modify on behalf of users and processes.
• Implement retrieval systems that bring relevant business data and documents into AI workflows under access constraints.
• Design safe interfaces that let AI agents invoke business functions, update records, and trigger downstream processes.
• Implement rule-based and declarative policy layers that govern agent behavior across workflows.
• Build comprehensive logging systems that capture AI decisions, data access, and actions for compliance and debugging.
• Establish feedback loops and observability practices to detect failures, measure quality, and improve agent performance over time.

Job description

Location: Remote (US-based) About the Role We are seeking a hands-on, technically deep Cloud Architect to serve as the critical technical bridge between our Engineering, Product Management, and Field teams. In this role, you will act as the primary subject matter expert on the cloud services we support, ensuring our product vision translates into technically sound, commercially viable solutions. You thrive at the intersection of technical execution and product strategy. You’ll guide Product Mana

Relevance is based on skills and concepts, not a guarantee that you qualify.

Coming-soon courses matched to this posting: outline, waitlist, and skill path.

Understand how governed AI systems differ from consumer AI and the key components that enable safe business process integration.

2. Identity, Access, and Permission Layers

Build authorization systems that control what AI agents can access and modify on behalf of users and processes.

3. Context Retrieval and Knowledge Integration

Implement retrieval systems that bring relevant business data and documents into AI workflows under access constraints.

4. Tool Interfaces and Action Execution

Design safe interfaces that let AI agents invoke business functions, update records, and trigger downstream processes.

5. Policy Engines and Constraint Systems

Implement rule-based and declarative policy layers that govern agent behavior across workflows.

6. Audit Logging and Traceability

Build comprehensive logging systems that capture AI decisions, data access, and actions for compliance and debugging.

7. Monitoring, Evaluation, and Continuous Improvement

Establish feedback loops and observability practices to detect failures, measure quality, and improve agent performance over time.

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