Lecturer/Snr Lecturer (AI-Augmented Product Architect)-Sch of Engineering

• Deliver lectures, tutorials and laboratory sessions across full-time Diploma programs (PET) and adult workforce upskilling modules (CET).
• Adopt diversified pedagogical frameworks, including project-based learning, flipped classrooms, and blended e-learning spaces.
• Supervise and mentor students during final-year projects, multi-disciplinary capstone assignments, and professional internship placements, particularly in Edge AI, IoT systems, and distributed architectures.

• Design, review, and revitalize instructional resources, module structures, and assessment rubrics aligned to emerging industry practices in Edge AI and distributed systems.
• Embed competencies such as AI model optimization and edge analytics into curriculum delivery.
• Develop hands-on lab modules involving embedded AI deployment, IoT telemetry pipelines, and hardware acceleration platforms.

• Establish partnerships with industry players in Edge AI, IoT, and smart infrastructure.
• Contribute to applied R&D and industry partnership in areas such as edge AI, digital twin systems can smart connected systems.
• Translate real-world deployments (e.g., edge inference, chiller optimisation, smart monitoring) into learning case studies.

• Mentor students in technical and career development within Edge AI and embedded systems domains.
• Perform academic administration duties including assessment tracking and quality assurance.

#LI-Hybrid

• Education: Degree or Postgraduate qualification in Computer Science, Computer Engineering, Information Systems, or related fields.

• Industry Experience: Minimum 3–5 years in software development, AI application development, or product engineering.

(Candidate should demonstrate strong proficiency in at least 2 of the following areas)

    i.    AI-Augmented Development & Low-Code/No-Code

• Experience with low-code/no-code and AI-assisted (“vibe coding”) tools to rapidly prototype web, mobile, and backend applications.

    ii.    Agentic AI & Workflow Orchestration

• Understanding of agentic AI concepts (reasoning, tool use, multi-step workflows) and ability to build agent-driven applications integrated with APIs or IoT systems.

    iii.   LLM Deployment (Cloud & Edge)

• Familiarity with deploying LLMs via cloud and/or edge (SLMs), including integration into applications and trade-offs (latency, cost, privacy).

    iv.   Cross-Platform Application Development

• Experience in building web and cross-OS mobile apps (iOS & Android) using frameworks such as Flutter or React Native, with simple, user-centric UI design.

    v.    AI Application Security & Risk Awareness

• Awareness of risks (e.g., prompt injection, data leakage, API vulnerabilities) and basic practices in secure AI application design and governance.