• Design and implement end-to-end ML systems, including data ingestion, feature processing, model training, and model serving • Architect and deploy scalable AI services supporting real-time and batch inference use cases • Build and maintain ML infrastructure across cloud environments (e.g., EC2, EKS, SageMaker, specialized inference hardware) • Develop and evolve MLOps platforms, including training pipelines, deployment workflows, feature stores, and model observability • Implement CI/CD and infrastructure-as-code patterns to automate model lifecycle management • Optimize model training and inference performance for cost, latency, and hardware efficiency • Monitor production ML systems for accuracy, reliability, and operational health • Partner cross-functionally with data engineering, architecture, governance, and security teams to ensure compliant and scalable solutions • Mentor team members on ML engineering, system design, and operational best practices • Contribute to special initiatives that advance AI platform maturity and engineering standards

• Act as the bridge between data infrastructure and customer-facing AI products. • Specialize in building low-latency API layers, production-grade RAG systems, complex ingestion pipelines, and Human-in-the-Loop workflows. • Work alongside Data Engineers to turn raw data lakes into live AI features.

• Design and build the foundational AI layers for broader engineering organization • Implement prompt frameworks and enforce deterministic structured outputs • Deploy production-grade Human-in-the-Loop state automations • Turn raw context into reliable, zero-hallucination agent-based actions

Role Description This course emphasizes the educator’s evolving role as observer and co-learner, with a focus on pedagogical documentation in K–6 settings. Through experiential and collaborative analysis of classroom practice, current research, and Ministry policy, teacher candidates will investigate how documentation and assessment can be used to design, interpret, and extend inquiry-based learning. The course foregrounds the educator’s role in making learning visible - to students, families, and colleagues - and in connecting inquiry to intentional, skills-based instruction. Teacher candidates will consider how equitable and culturally responsive practices are sustained when documentation and assessment are used as tools for reflection, collaboration, and responsive planning. Successful candidate will work with the course lead and/or team in order to provide consistency across sections. Qualifications - Master’s degree in Education - Bachelor of Education and/or K – 10 Teaching Certification - Experience in K-6 settings - Doctoral degree in Education - Post-secondary experience with focus on assessment and evaluation and/or inquiry-based research practices Requirements - Position Title: EU499 P VS: Learning Through Observation (Fall 2026) - Term: Fall 2026 - Dates of Term: September 8 - December 18th, 2026 - Mode of Delivery: Virtual Synchronous - Type of Instruction: Online - Days/Times: Thursday 5:30 – 6:50pm - Hours per week/Hours Total: 1.5/12 (0.25 credit) - Anticipated Class Size: 40 - Additional Course Requirements: None - Salary: $5106.20 - Application Deadline: June 25, 2026 Benefits - Required for All Applicants: - CV - Teaching Dossier - Candidate Application Form (CAF) - Required for External Applicants: - Names and Contact Information for Referees - Evidence of Good Teaching (e.g., teaching philosophy; assessment practices; scholarship that informs your teaching; evaluations) - Verification of highest degree - Optional: - Cover Letter - Teaching Dossier - Sample Course Outline