Part-time
8–10 committed hours / week
24 weeks. Plan for guided instruction, project work, reading, practice, and assessment activities alongside your existing commitments.
The AI Solutions Engineer Apprenticeship is a hands-on online programme for career switchers, junior developers, and recent graduates. Choose part-time delivery over 24 weeks or full-time delivery over 12 weeks, build foundational AI skills through practical projects, and graduate ready for junior-to-mid AI engineering roles.
Five core areas that form the foundation of practical AI engineering.
| Competency | What you'll learn |
|---|---|
| AI Fundamentals | Core concepts, machine learning basics, prompt engineering, and the landscape of AI tooling |
| Applied AI Development | Building with APIs, RAG pipelines, vector databases, and LLM integration patterns |
| Solution Architecture Basics | Designing AI workflows that solve real business problems, data flow design, and integration patterns |
| Introduction to Agentic Workflows | Multi-step agent orchestration, human-in-the-loop systems, and workflow automation |
| Responsible AI and Governance | PDPA compliance, bias awareness, ethical AI deployment, and data sovereignty fundamentals |
This programme is designed for learners building practical AI engineering capability.
| Audience | Background |
|---|---|
| Career Switchers | Professionals from non-technical fields looking to transition into AI roles |
| Junior Developers | 1–3 years of software development experience, new to AI/ML |
| Recent Graduates | Computer science or related degree holders seeking practical AI skills |
| Technical Professionals | QA, DevOps, or sysadmin professionals expanding into AI engineering |
Five progressive modules, each building on the last. Both schedules follow the same curriculum and practical outcomes.
| Module | Focus | Outcome |
|---|---|---|
| 1. AI Foundations | Core AI concepts, ML basics, prompt engineering, AI tooling landscape | Understand AI fundamentals and navigate the tooling ecosystem |
| 2. Applied Development | Building with APIs, RAG pipelines, vector databases, LLM integration | Build working AI applications with retrieval-augmented generation |
| 3. Solution Design | Business problem analysis, data flow design, integration patterns | Design AI solutions that solve real business problems end-to-end |
| 4. Agentic Workflows | Multi-step agent orchestration, human-in-the-loop, workflow automation | Build and deploy multi-step AI agent workflows |
| 5. Capstone Project | End-to-end project: design, build, test, and deploy a production AI system | Portfolio-ready project demonstrating full-stack AI engineering |
Fully online. Choose part-time delivery over 24 weeks or full-time delivery over 12 weeks, with hands-on projects at every stage.
Both formats cover the same learning outcomes, projects, and assessment standard. Full-time delivery compresses the same work into a shorter calendar period and requires protected study time.
24 weeks. Plan for guided instruction, project work, reading, practice, and assessment activities alongside your existing commitments.
12 weeks. The accelerated schedule suits learners who can protect larger blocks of time each week for practical development and project delivery.
| Requirement | Minimum / recommended setup |
|---|---|
| Computer | 64-bit PC or Mac with a modern multi-core CPU, 16 GB RAM recommended (32 GB is helpful for local containers or models), and at least 50 GB of free SSD space. |
| Operating system | A supported current Windows, macOS, or Linux host with permission to install development tools. Windows learners may use WSL2 where required by the course tooling. |
| Development tools | Python 3.11+, Git, a modern code editor, Docker Desktop or an equivalent container tool, and a current browser. Course tools may include OpenClaw, Ollama, n8n, FastAPI, Streamlit, and ChromaDB or Qdrant. |
| Internet and collaboration | Stable internet access of at least 20 Mbps, webcam and microphone for online sessions, and reliable access to the learning and collaboration platforms. |
| AI runtime | Instructor-approved local or cloud AI services may be used depending on the exercise. Local model execution is optional and can require additional memory, storage, or setup. |
| Access and safety | Ability to install or obtain support for the required tools, keep credentials private, and use synthetic or approved data in exercises rather than production secrets. |
No prior AI or machine learning experience is required. You need basic programming familiarity (any language) and a willingness to build practical AI engineering skills through guided projects.
The programme is delivered fully online. It runs part-time over 24 weeks or full-time over 12 weeks, and the fee is SGD 3,000 nett.
Plan for about 8–10 committed hours per week part-time or 18–20 hours per week full-time. You need a 64-bit PC or Mac, 16 GB RAM recommended, at least 50 GB of free SSD space, a current operating system, Python, Git, a code editor, Docker or an equivalent container tool, and reliable internet. Running local AI models may require additional memory and storage.
You'll work with OpenClaw, Ollama, n8n, FastAPI, Streamlit, ChromaDB/Qdrant, Docker Compose, and production deployment patterns — the same stack we use for client projects.
Yes. Upon successful completion of the capstone project, you'll receive a Digital Futures Academy Certificate of Completion and Digital Badge verifying your competencies.
Book a call to discuss the apprenticeship programme and whether it's right for you.