New opportunity

Lecturer/Senior Lecturer (AI) - School of Information Technology

Nanyang Polytechnic · Singapore

About this role

As academic staff at Nanyang Polytechnic, you will play an active role in the academic, professional, and personal development of learners to prepare them for work and life. This role involves designing, evaluating, and enhancing course curricula and co-developing training content with industry partners to ensure relevance to evolving AI industry needs. Responsibilities include teaching and project supervision of PET and CET courses, including professional short courses, as well as spearheading new opportunities, initiatives, and collaborations with the industry. You will also plan, lead, manage, and undertake AI project development with industry partners. Requirements include at least 3 years of relevant experience in IT or software development and relevant qualifications in Computer Science, Engineering, Information Technology, or related fields. Candidates should possess a strong technical background in software development, including front-end, back-end, full-stack, and software architecture. Proficiency in traditional AI, generative/agentic AI, and cloud technologies is required, along with good communication and interpersonal skills to connect effectively with diverse learners. Preference will be given to candidates with practical experience in designing and implementing AI solutions in an industry setting.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Software DevelopmentFront-end DevelopmentBack-end DevelopmentFull Stack DevelopmentSoftware ArchitectureTraditional AIGenerative AIAgentic AICloud TechnologiesCurriculum DesignProject Supervision

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Curriculum Development and Industry Alignment
The role requires designing and enhancing course curricula to remain relevant to the rapidly evolving AI industry. You must actively co-develop training content with industry partners to ensure that the educational material reflects current market needs and practical applications.
Industry-Linked AI Project Leadership
Beyond teaching, you are expected to plan, lead, and manage AI projects in collaboration with industry partners. This requires bridging the gap between academic theory and real-world implementation, utilizing your background in full-stack development and cloud technologies.
Pedagogical Engagement and Mentorship
You must facilitate the academic, professional, and personal development of diverse learners. This involves teaching both PET (Pre-Employment Training) and CET (Continuing Education and Training) courses, requiring the ability to communicate complex technical concepts with empathy and clarity.

Likely challenges to think through

Balancing Academic Rigor with Rapid AI Evolution
The fast-paced nature of generative and agentic AI makes it difficult to maintain static course materials. Prepare by demonstrating how you stay updated on emerging technologies and how you would modularize curriculum to allow for frequent, agile updates without compromising foundational learning.
Bridging Industry Expectations and Student Capability
Managing industry-led projects while teaching students with varying skill levels presents a significant coordination challenge. Prepare by articulating a strategy for project supervision that ensures industry deliverables are met while providing students with appropriate scaffolding and learning opportunities.

Practice your answers

Technical practice

Architecting Scalable AI Solutions

What to coverDiscuss the trade-offs between monolithic and microservices architectures when deploying AI models. Explain how you would design a system to handle high-concurrency requests while maintaining low latency for inference.

Generative vs. Agentic AI Implementation

What to coverExplain the fundamental differences between traditional machine learning, generative AI, and agentic AI workflows. Provide an example of how you would integrate an autonomous agent into a software application to solve a specific business problem.

Cloud Infrastructure for AI

What to coverDescribe your experience with cloud-native AI services. How do you manage the lifecycle of an AI model, from training and versioning to deployment and monitoring in a cloud environment?

Full-Stack AI Integration

What to coverHow do you ensure seamless communication between a front-end interface and a back-end AI service? Address concerns regarding API security, data validation, and state management when building AI-powered web applications.

Curriculum Design for Emerging Tech

What to coverWhen designing a course on rapidly evolving AI technologies, how do you balance theoretical foundations with practical, industry-relevant skills? Suggest a framework for keeping course content current with industry trends.

AI Project Lifecycle Management

What to coverDescribe your approach to managing an AI project from inception to delivery. How do you define success metrics for an industry-partnered project, and how do you handle technical debt during development?

Software Development Best Practices

What to coverHow do you enforce coding standards and software architecture principles in a team environment? Discuss how you would mentor students or junior developers to write maintainable, scalable code.

Behavioural practice

Bridging Industry and Academia

What to coverDescribe a situation where you had to collaborate with an external partner to achieve a project goal. How did you manage expectations and ensure the output met both academic standards and industry requirements?

Adapting Teaching for Diverse Learners

What to coverProvide an example of how you adjusted your teaching style to support a student or learner who was struggling with a complex technical concept. What was the outcome of your intervention?

Leading Through Technical Challenges

What to coverTell me about a time you led a project that faced significant technical roadblocks. How did you identify the root cause, communicate the issue to stakeholders, and pivot to a successful solution?

Managing Conflicting Priorities

What to coverAs a lecturer, you may balance teaching, curriculum development, and industry projects. Describe a time you had to manage competing deadlines. How did you prioritize your tasks to ensure quality in all areas?

Sharpen your knowledge

Topics to prepare

Curriculum Design and Pedagogy
The role requires designing and enhancing course curricula for both PET and CET learners. Exercise: Draft a lesson plan for a 3-hour professional short course on Generative AI, including learning objectives, a hands-on activity, and an assessment method. This is relevant to the requirement of developing training content that remains relevant to industry needs.
Agentic AI Architectures
The position demands proficiency in agentic AI. Exercise: Research and document the workflow of an autonomous agent system (e.g., using LangChain or AutoGPT) that solves a specific business task. This is relevant to the requirement of implementing AI solutions in an industry setting.

Skills to revise

Full-Stack AI Integration
You must demonstrate a strong technical background in software development alongside AI proficiency. Exercise: Build a simple web application that connects a front-end interface to a back-end API which consumes an LLM service. This is relevant to the requirement for full-stack development and AI solution implementation.
Cloud-Based AI Deployment
Proficiency in cloud technologies is required for project development. Exercise: Deploy a containerized AI model on a cloud platform (e.g., AWS, Azure, or GCP) and expose it via a REST endpoint. This is relevant to the requirement of planning and managing AI projects with industry partners.
Industry Project Management
The role involves leading and managing AI projects with industry partners. Exercise: Create a project proposal template that outlines scope, technical milestones, and industry deliverables for a hypothetical AI collaboration. This is relevant to the requirement of spearheading new industry initiatives.

Your preparation checklist

Prepare a portfolio of industry-applied AI projects
Compile documentation or case studies of AI solutions you have designed and implemented in a professional setting. Focus on projects that demonstrate your proficiency in traditional, generative, and agentic AI, specifically highlighting how these solutions addressed real-world business problems.
Develop a sample curriculum module
Create a brief lesson plan or training outline for a hypothetical short course on a current AI topic. This should demonstrate your ability to translate complex technical concepts into accessible content for diverse learners, aligning with the polytechnic's goal of preparing students for industry needs.
Articulate your full-stack and cloud expertise
Prepare to discuss your experience with software architecture, front-end, and back-end development in the context of AI deployment. Be ready to explain how you integrate AI models into cloud-based applications and manage the end-to-end development lifecycle.
Formulate strategies for industry collaboration
Draft a proposal or talking points on how you would identify and spearhead new industry partnerships. Consider how you would bridge the gap between academic research and practical industry application to enhance student learning and project outcomes.
Prepare questions regarding pedagogy and industry alignment
Develop thoughtful questions for the interviewers about how the school balances academic rigor with the rapid evolution of AI technologies. Ask about the current process for co-developing content with industry partners to ensure you understand the collaborative expectations of the role.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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