AIAP® (industry)
Accelerated Path to Industry-Ready AI Engineers
Building on the AIAP methodology, AI Singapore (AISG) and the Infocomm Media Development Authority (IMDA) have launched the 6-month AIAP (Industry), AIAP(I), to train industry-ready AI Engineers and expand Singapore’s AI talent pipeline.
Programme Prerequisites
Foundational proficiency in AI and Software engineering skills, and obtain a pass in the Technical Assessment and Interview
Learning Outcomes
Build business-relevant AI projects: Master ML pipelines, software engineering, and basic deployment
Programme Structure
6-month, in-person,
full-time programme
with a monthly stipend
Application Process for AIAP(i)
Application Process for AIAP(i)
For Aspiring AI Engineers with basic proficiency in AI and Software engineering skills.
AIAP(I) Selection Test
Stage 1: Technical Assessment
6-day take-home technical assessment assessing EDA, ML, and software engineering proficiency based on a practical AI problem statement
Stage 2: Interview
Assessment involves a 1-on-1 interview and a collaborative group case study
Practice your skills: Attempt our past technical assessment here
Real-world AI Experience
Trainees will get to learn how to design and develop deployable AI projects that meet business deliverables, master end-to-end machine learning pipelines, intermediate software engineering, and basic model deployment.
9 am – 6 pm
Industry Readiness Phase
Structured 6-month Journey Consisting of 2 Phases
Acquire fundamental AI, software engineering and MLOps skills
On-the-job training, working on real-world AI projects
Phase 1: Skilling Phase (3-Month)
Project-based Learning to Master end-to-end Machine Learning pipelines, software engineering, and model deployment to build practical, industry-relevant AI projects across core domains.
Classical Models
MLOps
ISO, Ethics & Governance
Artificial Neural Network
Computer Vision
Time-series
Natural Language Processing
Large Language Modeling
Phase 2: Industry Readiness Phase (3-Month)
Collaborate in teams of apprentices, led by a full-time engineer and supported by Project Managers and the MLOps team, to design, develop, and deliver practical, business-relevant AI projects for real-world industry challenges over 4 development sprints.
