Certified Generative AI Engineer
A proctored HIGAET credential that validates end-to-end Generative AI engineering competence across design, build, and operate.
Duration
Self-paced exam window · 5-7 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Generative AI engineering is the end-to-end practice of designing, building, and operating AI systems that combine models, prompts, retrieval, tools, evals, and safety controls. This credential matters now because employers need proof that an engineer can carry a system across design, build, and operate rather than just write a clever prompt.
It is used to deliver retrieval-grounded applications with structured outputs, evaluation harnesses, and monitored deployments with versioning and rollback. It does not solve shallow competence: a certificate cannot substitute for debugging real traces, and passing an assessment does not guarantee every future system will succeed without ongoing evals.
By the end the candidate will be able to produce a retrieval-grounded application with cited structured outputs, a repeatable evaluation and safety suite, and a versioned deployment with observability and rollback demonstrated under assessment conditions.
Why this course exists
The gap is between scattered tutorials and validated competence across the whole arc from Model and Prompt through Context, Retrieval, Tools, Evaluation, Security, and Production. The credential exists to verify that arc in one proctored assessment spanning design, build, and operate.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and REST APIs
- Experience building LLM prototypes
- Familiarity with deployment and monitoring basics
Technologies & tools
Skills you'll gain
What you'll be able to do.
- Earn the HIGAET Certified Generative AI Engineer credential.
- Demonstrate competence across architecture, evaluation, and operations.
- Receive a verifiable digital badge accepted by HIGAET hiring partners.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Designed AI solution blueprint
- Project 02
Built retrieval-grounded application
- Project 03
Evaluation and safety test suite
- Project 04
Operated deployment with monitoring
- Capstone
End-to-end Generative AI engineering assessment
Speak the language first.
- Solution design for AI systems
- Solution design maps a business need to model choice, retrieval, tools, and safety checks before building.
- Prompt frameworks
- Prompt frameworks standardize instructions, examples, and output schemas so behavior stays consistent.
- Retrieval-grounded applications
- Retrieval-grounded applications pair a model with document search so answers stay current and checkable.
- Evaluation harnesses
- Evaluation harnesses run repeatable test suites that score quality and catch regressions.
- Deployment platforms
- Deployment platforms package and release AI services with versioning and rollback support.
- Observability tooling
- Observability tooling captures prompts, outputs, and errors so failures can be traced and fixed.
- Safety controls
- Safety controls such as content filters and output checks block harmful or off-policy responses.
- API integration patterns
- API patterns cover keys, retries, and timeouts so model and tool calls behave reliably in production.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Assessment build fails end-to-end under time pressure
Scope to the smallest working slice first, then add retrieval and evals once the base path runs.
Grounded application returns unfaithful answers
Verify retrieved passages before generation and constrain the prompt to cite only provided sources.
Evaluation suite flags regressions late
Run the harness after every change and triage failures by step so the cause is isolated early.
Deployed demo errors on reviewer traffic
Check gateway logs for rate limits and timeouts, then add retries and a fallback response path.
Monitoring shows silent quality drift
Compare recent outputs to the golden set and refresh test cases with newly observed failures.
Before you move on, you should be able to
- Design end-to-end Generative AI solutions from need to architecture
- Build retrieval-grounded applications with structured outputs
- Evaluate systems with repeatable quality and safety suites
- Deploy versioned services with monitoring and rollback
- Operate live systems using traces and incident checks
- Demonstrate competence across design, build, and operate tasks
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
Ready to start Certified Generative AI Engineer?
A Self-paced exam window course — Certifications.