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.