AI Engineer (LLM applications)
Real portfolio pieces built during the course, not a certificate for its own sake. Each one is work you can show.
- Evaluation planHow an AI assistant is judged before launch: a set of real questions with known answers, what counts as correct, what it must decline, and the result needed to go ahead.
- Tool authorisation designHow the assistant's order lookup decides whose orders it may return: taken from the signed-in session, enforced in code, checked twice, tested against the attack that worked, and blind to instructions in text.
- Cost and latency noteWhat the assistant costs per conversation and per month, where its slowness comes from, what to change, and what finance should budget beyond the model bill.
- Readiness reportThe case for the CTO: what the assistant does and does not do, the evidence from evaluation and pilot, what went wrong and was fixed, the controls, the cost, and a scoped recommendation with its next gate.
What you'll learn
Course content · 7 modules, 20 lessons
Sign up to unlock every lesson - the titles below show exactly what is inside.
What an AI engineer adds to a developer's skills, how LLM applications are put together, and how to scope one.
Requirements
- Comfortable with the fundamentals this course's own Module 1 covers, or equivalent experience.
- No prior experience in this field is required to start.
- A computer with a reliable internet connection.
- Comfortable using a web browser - no software to install.
Description
Every CertClue course follows the same seven-part shape: fundamentals, the role translated out of job-posting language, a real working day, the job's recurring rhythms, a multi-day simulation, the portfolio you build along the way, and a handoff into your next move. Here is what that looks like for ai engineer (llm applications).
Who this course is for
Anyone aiming to become a ai engineer (llm applications), including career changers with no background in it yet. This is the entry rung of a realistic ladder:
Builds and maintains server-side code and APIs that other people and systems use.
Builds applications on large language models that can be trusted: evaluated before launch, grounded in the organisation's data, fenced by code, and watched in production.
Designs how an organisation builds with AI: shared platforms, evaluation standards, and which use cases are worth doing.
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