Applied AI pathway
Applied AI and ML systems, built against real employer demand.
AI.SPIRE is a completed Applied AI and ML Systems pathway, delivered in Jordan with local partners. It moved learners from AI pre-work into deployed systems: databases, predictive models, NLP, RAG, knowledge graphs, APIs, Docker, monitoring, and capstone demonstrations partners could inspect. It now serves as one proof point for how LevelUp designs market-relevant technical pathways that can be adapted for other regions, institutions, and employer coalitions.
At a glance
Three partners, each with a defined role.
LevelUp contributes curriculum design, delivery expertise, and systems strategy alongside local operating and funding partners.
- Partners
- LevelUp Economy (curriculum and delivery), Istidama Consulting (local operations), and the Future Skills Fund (funding and oversight).
- Implementation
- Jordan, delivered hybrid with instructors in the United States and Jordan.
- Result
- 51 learners enrolled; 48 earned certificates of completion. Twelve capstone systems were deployed.
- Status
- Delivered, July 2026. AI.SPIRE was the first pathway run through Darb.Tech, one implementation vehicle for applied technical pathways.
Delivery model
How AI.SPIRE moved from training to economic capability.
LevelUp provided curriculum development, program design, and delivery expertise while guiding the wider systems approach around AI capability, entrepreneurship, and employer adoption. The same four phases are what a partner in another market would adapt.
Phase 01
Build the technical operating base
Learners start with Git, Python, SQL, data analysis, testing habits, and reproducible workflows before moving into deeper AI systems work.
Phase 02
Build applied AI system components
Project-based modules move through predictive modeling, NLP, multilingual text handling, RAG, vector retrieval, and knowledge graph enrichment.
Phase 03
Deploy, monitor, and explain the work
Learners integrate FastAPI services, Docker Compose, monitoring, automated evaluation, documentation, and executive communication into portfolio artifacts.
Phase 04
Connect employers and ventures
Employer needs and market opportunities shape capstones that can lead toward jobs, internal innovation, or new company formation.
Portfolio evidence
Every module produced an inspectable artifact.
The pathway was designed so that a partner, an employer, or a funder can look at the work directly.
- GitHub pull requests reviewed against the same standards a working team would apply
- Labs and integration tasks that build on one another
- Deployed APIs and reproducible data pipelines
- Automated evaluation routines and written documentation
- An executive-ready capstone briefing partners can sit in on
Formats
The same architecture can stretch or compress.
The delivered implementation is one shape of the pathway. Partners in other markets, with different timelines, entry points, or institutional constraints, can take another.
As delivered
Applied intensive
A project-based pathway for engineers and technical professionals who need working capability on a short timeline.
Extended
Two-year applied pathway
Partners can extend the same architecture into a two-year applied diploma or degree where academic progression and learner support matter more than speed.
Compressed
Workshops and primers
Shorter applied AI and ML workshops for teams where the full intensive is not the right starting point.
Related
Where this sits in the rest of the work.
Curriculum
Applied AI and Machine Learning Systems
The catalog course the AI.SPIRE pathway is built from.
Initiative
Future Skills Fund
The funding and oversight structure behind this implementation.
Insight
The AI.SPIRE launch note
Why the pathway was designed this way, and what it tested.
Capability area
Build talent systems
The pillar AI.SPIRE belongs to: talent, employers, capital, and institutions reinforcing one another.
Next step
Run this pathway in your market.
Employers can shape capstones and hire from a cohort. Institutions can adapt the architecture. Funders can back the next implementation, wherever it runs.