Cloud Engineer

Job Description.

Virtual Learning Infrastructure: Ingesting and expanding Digital/AI Literacy lessons into Skyline to establish a universal Gen Ed baseline.

AI Readiness Framework: Developing a data-driven model and assessment strategy to measure individual school and district AI readiness levels.

Reporting & Governance: Deploying data collection workflows and a public-facing dashboard to provide a transparent baseline for improvement.

Physical Infrastructure: Launching the Innovation & Engagement Center (CoE) lab and completing the Scoping Report for future regional Hubs.

Strategic Roadmap: Finalizing the Labor Market Analysis, Learning Pathways Blueprint, and Tiered Support Strategy for schools.

Role 1 - AI Solutions Engineer:

Technical Architecture & Backend Development: Design, develop, and deploy sophisticated AI models and applications, focusing on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures.

Cloud Infrastructure Management: Manage and optimize AI workloads across diverse cloud environments including Vertex AI, Azure AI, AWS, and OCI.

System Integration: Develop secure APIs and microservices to integrate AI capabilities into existing district platforms such as Student Information Systems (SIS) and Learning Management Systems (LMS).

Collaborative Implementation: Partner daily with the Enterprise AI Design Specialist to translate human-centered design prototypes into functional technical builds for skAI and CPS ASSIST.

Operational Alignment: Coordinate with the Enterprise AI Program Specialist to ensure technical features align with the AI University roadmap and district-wide training capacities.

Data Engineering for AI: Implement efficient data retrieval mechanisms, manage vector databases, and maintain knowledge graphs to support AI accuracy and performance.

Security & Scalability: Utilize Infrastructure as Code (IaC) and CI/CD pipelines to ensure AI deployments are automated, reproducible, and strictly follow district security and privacy standards.

Facilitate Human-Centered Co-Design: Lead the Co-Design Initiative, partnering with teachers and students in UX focus groups to ensure all AI modules are culturally responsive, practically relevant, and pedagogically sound.

Automate Workflow Solutions: Design and implement functional Gems (custom Gemini models) and NotebookLM instances for Central Office departments to automate repetitive administrative tasks and improve data retrieval.

Maintain the Instructional Knowledge Graph: Oversee the Instructional Knowledge Graph, ensuring AI competencies are mapped across core academic standards and proactively updating materials as technology evolves.

Ensure Ethical Design Integrity: Apply the CPS AI Principles (Equitable, Transparent, Human-Centered, Sustainable) and AI Readiness Framework to all designs, prioritizing accessibility for diverse learners and English Learners.

Deliverables

Integrated AI Literacy Modules: A Gen Ed AI curriculum fully ingested into the Skyline platform for universal district access.

Enhanced Skyline User Profiles: Technical build-out of Teacher-as-Learner and Parent-as-Learner capabilities to support role-specific tracking and personalized learning pathways.

AI Readiness Dashboard: A public-facing scoring engine and data visualization tool used to report school-level AI literacy

Digital Credentialing Hub: A technical solution for deploying a district AI badge program with portable, open-standard credentials.

Automated Data Collection Workflows: Integrated systems within the data warehouse to ingest existing data for real-time readiness reporting.