The AI & Data Engineer role builds a sustainable, future-ready AI engineering capability at Singapore American School. This role is responsible for integrating core school systems, unlocking the value of our enterprise data lake, and developing scalable AI-enabled tools that reduce friction for educators and amplify their impact. The Engineer position will design production-ready AI and data products that create more moments of magic for students and educators, while advancing SAS’s strategic commitments to excellence, extraordinary care, and possibilities.

Reporting to SAS technology leadership and working in close partnership with Teaching & Learning, Data Governance, and IT Operations. The role collaborates deeply with academic leaders, the AI Education Specialists, and cross-functional teams to ensure AI solutions directly support instructional needs, workflow efficiency, and long-term capability building.

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.

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.