AI Engineer Bootcamp
A 16-week cohort that takes working engineers from competent coders to job-ready Generative AI engineers.
Duration
16 weeks · 5-7 hours/week
Level
Intermediate
Delivery
Hybrid
Status
Open for enrollment
Why this technology matters.
AI engineering is the software discipline of shipping LLM-powered applications in Python, from APIs and orchestration frameworks through retrieval, evals, and deployment. It matters now because working engineers who can integrate models into tested, deployed services are the bottleneck for real adoption.
It is used to build grounded assistants and workflow tools inside a retailer, a support team, or an internal ops group, solving the jump from scripts to maintained services with reviews and tests. It does not solve fundamentals by itself: frameworks do not fix weak Python or missing evals, and a portfolio does not replace continued practice after the cohort.
By the end the student will be able to build a tested Python LLM application with API structure, a retrieval-grounded assistant wired through orchestration, and a deployed versioned service with evaluation results and a documented portfolio narrative.
Why this course exists
The gap is between a competent coder and a Generative AI engineer who can design, evaluate, deploy, and explain a system end to end. The bootcamp teaches the arc from Model and Prompt through Context, Retrieval, Tools, Evaluation, and Production, with collaboration and communication layered on like a real team.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Working proficiency in Python and Git
- Experience building web APIs
- Ability to commit to cohort schedule and reviews
Technologies & tools
Skills you'll gain
A 16 weeks arc, module by module.
- Module 01
Phase 1 — Foundations and tooling (weeks 1–4)
- Module 02
Phase 2 — Applied LLM systems (weeks 5–8)
- Module 03
Phase 3 — Retrieval, agents, and evaluation (weeks 9–12)
- Module 04
Phase 4 — Capstone, interviews, and placement (weeks 13–16)
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Ship four portfolio-grade Generative AI projects with HIGAET mentorship.
- Build a hiring-ready GitHub, resume, and interview narrative.
- Access HIGAET's global partner hiring network upon completion.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
LLM application build
- Project 02
Retrieval-grounded assistant
- Project 03
Evaluated deployment project
- Project 04
Open-source contribution project
- Capstone
Generative AI engineer portfolio with interview narrative
Speak the language first.
- Python application building
- Python application building covers structuring code, APIs, and tests so AI features run reliably.
- LLM integration
- LLM integration connects applications to models through prompts, parameters, and response handling.
- Orchestration frameworks
- Orchestration frameworks organize multi-step model, retrieval, and tool flows into maintainable code.
- Retrieval systems
- Retrieval systems supply relevant documents to the model so answers stay grounded.
- Evaluation practices
- Evaluation practices test builds against expected cases to prove quality before sharing.
- Deployment workflows
- Deployment workflows publish services with versioning, checks, and rollback paths.
- Version control collaboration
- Version control with reviews keeps team code clean and changes traceable.
- Technical communication
- Technical communication explains builds, trade-offs, and results clearly in portfolios and interviews.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Bootcamp build stalls across too many features
Cut scope to one working demo path, then layer retrieval and evals in weekly milestones.
Retrieval-grounded demo gives weak answers
Check chunk quality and retrieved passages first, then tighten prompts before changing models.
Deployment breaks the night before review
Freeze features early, deploy from a clean checkout, and verify the live path with a smoke script.
Code reviews surface repeated basic issues
Adopt a short checklist for tests, naming, and error handling and clear it before requesting review.
Portfolio pieces look unfinished to reviewers
Add a readme with problem, architecture, eval results, and a live demo link for each project.
Before you move on, you should be able to
- Build LLM applications in Python with tested APIs
- Integrate retrieval and orchestration into working assistants
- Evaluate builds and iterate from test results
- Deploy versioned services with monitoring basics
- Collaborate through reviews and open-source workflows
- Present a portfolio and interview narrative with evidence
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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A proctored HIGAET credential that validates end-to-end Generative AI engineering competence across design, build, and operate.
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