Position Responsibilities
The specific roles and responsibilities are subject to change and evolve and may include but not be limited to any of the following areas:
AI & Data Product Development
- Design, build, and deploy advanced analytics and machine learning models aligned to SAS strategic priorities, including decision-support systems, workflow automation, recommender systems, and advanced data visualizations that improve teaching, learning, and operations.
- Lead development of AI copilots, agentic workflows, and RAG-based tools that help staff and educators interrogate information, improve productivity, and make evidence-informed decisions.
- Build evaluation, observability, and monitoring pipelines to ensure AI models remain accurate, safe, fair, and aligned with SAS policy and ethical guidelines.
Enterprise Data Architecture
- Create and maintain a highly automated ETL pipeline ecosystem that integrates all major school systems (SIS/LMS, assessment platforms, HR/Finance, operations systems, and third-party tools).
- Architect and optimize the SAS data lake with strong data models, lineage, and governance to support real-time analytics and future AI integrations.
- Partner with Data Governance to ensure data quality, documentation, and long-term maintainability of the school's data infrastructure.
Governance, Safety & Security-by-Design
- Embed ethical, secure, and privacy-preserving AI practices across all engineering work, including: data minimization, anonymization, PII protection, guardrails, and secure access controls.
- Implement robust evaluation frameworks for safety, bias detection, and responsible use of generative and predictive systems.
- Ensure all systems and integrations meet SAS policies, regulatory requirements, and industry best practices for responsible innovation.
Cross-Functional Collaboration
- Work closely with the Educational Technology team to translate instructional problems into technical solutions that support assessment, personalization, student progress insights, and instructional planning.
- Collaborate with IT Operations to ensure infrastructure, deployment, and security models align with scalable cloud-first practices.
- Engage with external vendors, research partners, and EdTech organisations to benchmark SAS against emerging trends and inform future design.
Other Responsibilities
- Participate in professional learning opportunities, staying abreast of trends in AI and emerging technologies and their application in education.
- Complete other duties as assigned by the Technology Leadership Team.
Position Requirements & Qualifications
Required Education & Background
- Bachelor's Degree in Computer Science, Mathematics, Software Engineering, Computer Engineering, or related field.
- Proven track record delivering production-grade AI systems, including generative AI, RAG, or agentic workflows, from discovery through deployment and ongoing monitoring.
- Strong English communication skills with the ability to translate technical complexity into clear, actionable insights for non-technical partners.
Required Technical Experience
- Programming & Frameworks: Python JavaScript/TypeScript modern AI/ML frameworks (e.g., LangChain/LangGraph) API development (REST/GraphQL) microservices and serverless patterns.
- AI/ML & Data Engineering: LLM tooling (prompting, RAG, safety/guardrails), vector stores (e.g., FAISS/Pinecone),, data warehouses (e.g., BigQuery), dashboarding (Looker Studio), feature stores, and automated data pipelines.
- Cloud & Infrastructure: Cloud platforms (GCP/AWS/Azure), containerization (Docker/Kubernetes), CI/CD pipelines, observability, and secure, cost-efficient deployments.
- Systems Integration: Experience integrating enterprise systems, ideally SIS/LMS, assessment platforms, authentication/SSO using secure APIs, webhooks, and data contracts.
Preferred
- Advanced degree in a relevant field
- Prior work in EdTech, K-12, higher education, or social-impact contexts, especially building tools that improve instructional time, assessment, student support, or school operations.
- Background working with UX/UI designers to create educator-friendly products with high adoption and usability.
- Familiarity with data governance, AI policy, digital citizenship, or responsible innovation frameworks.
- Knowledge of multi-modal AI, simulation environments, agentic orchestration, or frontier model evaluation practices.
