10 Weeks Duration
Real Infrastructure
Paid From Day One
Selective Cohort
Our engineers put AI into
production across Singapore and the US
Aleph trains AI engineers who build, deploy, and monitor the models and systems organizations depend on, across both public and private sectors.
SGTech Workforce Transformation Award 2025
AI is moving from pilot to production
Organizations from banks to government agencies are moving AI out of experiments and into real operations. Every model that ships needs engineers who can fine-tune it, serve it, monitor it, and connect it to the systems around it.
Singapore15,000
AI practitioners Singapore aims to build, more than triple the current pool of about 4,500.
Source: National AI Strategy 2.0, MDDI
70.4%
Of larger enterprises used AI in 2025, up from 62.5% the year before. AI and data are among the fastest-growing tech roles.
Source: IMDA Singapore Digital Economy Report, Oct 2026
United States34%
Projected job growth for data scientists from 2024 to 2034, much faster than the average for all occupations.
Source: U.S. Bureau of Labor Statistics
88%
Of organizations use AI in at least one business function, yet only 7% have fully scaled it.
Source: McKinsey, The State of AI in 2025
Learn AI engineering by doing AI engineering
With mentor guidance, you apply AI techniques to real operational challenges, designing, training and deploying models the way production teams do.
Build real models
Hands-on projects from week one: analyse data, train and fine-tune models, and ship them to production.
Learn from industry experts
A 1:6 trainer-to-trainee ratio, plus career mentorship from practitioners.
Meet your future employers
Career sessions with placement partners to gain industry insight and build your network.
Keep your skills current
Return for one week of advanced training every six months as models and tools evolve.
Roles built on data and models
AI is creating new roles at the intersection of software, data, and infrastructure. The programme gives you the technical foundation to grow into AI engineering roles across the public and private sectors.
Nicole Cheong
AI Engineer
Build and ship AI-powered features, from choosing and fine-tuning models to running them in production.
Skills
Jun Cheng
Machine Learning Engineer
Train, evaluate and improve models for classification, vision and prediction problems.
Skills
Luke Chen
AI Platform Engineer
Run the infrastructure that serves models reliably, quickly and at scale.
Skills
Jabez Tay
Applied Data Engineer
Build the data foundations AI systems need to learn, measure and perform.
Skills
Jabez Tay
MLOps Engineer
Monitor, optimize and integrate models once they are live, keeping them accurate and efficient.
Skills
One foundation. Multiple paths forward.
Twelve weeks from first script
to production model
Nine modules covering Python, cloud infrastructure, data, machine learning, LLMs and model serving, then a capstone.
Foundations
WEEKS 1–2
WEEKS 1–2
Python for AI Engineers
Infrastructure & Data
WEEKS 3–5
WEEK 3
Infrastructure: AWS and Containerization
WEEK 4
Databases: SQL, PostgreSQL, MongoDB
WEEK 5
1D and 2D Data Analysis
AI & Machine Learning
WEEKS 6–9
WEEKS 6–7
Intro to AI and ML: Foundation Models for LLMs, Classification, Responsible AI
WEEK 8
Fine-tuning and AI Systems with Hugging Face and Ollama
WEEK 9
Vision and Graph Models
Deployment & Operations
WEEKS 10–11
WEEK 10
Model Serving: vLLM, NVIDIA Triton and NIM, AWS SageMaker
WEEK 11
Optimization, Monitoring and Integration
Final Capstone
WEEK 12
WEEK 12
Capstone: build, deploy and present an end-to-end AI system
The platforms and tools AI teams use every day
Get hands-on with the same frameworks and serving stacks used in production.
Programming
PythonCloud and infrastructure
AWSDatabases
SQLPostgreSQL
MongoDBModels & fine-tuning
Hugging Face
OllamaModel serving
NVIDIA TritonNVIDIA NIMML platform
AWS SageMakerResponsible AI
Operations
Start where you are
Whether you are new to the field or switching careers, both tracks lead to a career in AI engineering.
No experience required
New to Tech
Apply if you:
Are a recent graduate or new to IT; enjoy working with data and solving problems logically; want structured training from the ground up; are ready for a full-time career in tech.
In the cohort
Learn Python, data and machine learning from the fundamentals, then move into a full-time placement with ongoing mentorship.
Mid-career welcome
Career Switcher
Apply if you:
Are a working professional ready for a change; have some exposure to data, analytics, IT or software to build on; want to move into AI engineering; want a paid, structured route into a new career.
In the cohort
Turn your existing experience into AI engineering skills, then move into a placement with a partner organization.
Selection steps
Logical Aptitude Test
A pseudo-language developed by our trainers.
Analytical Test
Analyse data and reason your way to a conclusion.
Interview
We assess your aptitude and ability to apply concepts.
Both tracks require
Earn a paycheck while you learn the cloud
Many career transition courses leave you to find a job on your own after you finish. Here, you are a full-time employee from day one, and you train toward a placement with a partner organization.
Paid from day one
Monthly training allowance with CPF contributions.
No tuition
Training is fully sponsored, with a $0 programme fee.
Expert mentorship
Career guidance and professional development resources from practitioners.
Employee benefits
Insurance and full employee benefits from day one.
Full time placement
A three-year role with a partner organization, with a monthly salary.
Long term growth
One week of advanced training every six months during your placement.
Learn from engineers who ship AI systems
Your trainers build, deploy and run AI systems, and bring real production experience into every module. At a 1:6 ratio, you get close guidance while training alongside recent graduates and career switchers building the same skills.
Questions about becoming
an AI Engineer
AI Engineers build and run the systems that put models to work. They prepare data, train and fine-tune models, deploy them to production, and keep them accurate, efficient and responsible once they are live.
No. You learn Python from the fundamentals in the first weeks of the program, then build on it with data, machine learning and model serving. What matters is strong aptitude and logical problem solving, which the selection tests assess.
Python, AWS with containers, SQL, PostgreSQL and MongoDB, Hugging Face and Ollama for models and fine-tuning, vLLM, NVIDIA Triton and NVIDIA NIM for model serving, and AWS SageMaker, plus responsible AI practices, monitoring and optimization.
Yes. You are a full-time employee from day one, with a monthly training allowance with CPF contributions, insurance and full employee benefits. Training is fully sponsored, with a $0 programme fee.
Training is full-time, 9am to 6pm, Monday to Friday.
No. The program is full-time from day one, and you are employed for the whole training period.
After the training you move into a three-year role with a partner organization, with a monthly salary, and return for one week of advanced training every six months during your placement.
Recent graduates, people new to IT and working professionals switching careers. No degree or prior experience is required. The Singapore cohort is open to Singapore Citizens and PRs, and you need to commit full-time to the training and the three-year placement.
Selection has three steps: a logical aptitude test, an analytical test and an interview. The full process can take up to 1.5 months.
Your path starts here.
Applications are open for the November 2026 cohort. Seats are limited.
