AI engineer (RAG/LLM)
Build smart assistants powered by LLMs
Who is an AI engineer (RAG/LLM)?
RAG/LLM engineers build retrieval-augmented assistants—they shape ingestion, embeddings, retrieval, prompting, and evaluation while controlling spend and risk.
The role
- Architects ingestion, embedding, retrieval, and answering pipelines.
- Benchmarks retrieval and answer quality each release to catch regressions.
Fit in the team
- Partners legal and product on sensitive corpora and policies.
- Works with platform and finance so token or GPU bursts stay visible.
- Aligns data engineering on freshness of indexed knowledge.
Skills that matter early
- Vector database tuning balancing recall and precision.
- Prompt templates that ground domain vocabulary.
- Hybrid retrieval mixing dense vectors and lexical search.
- Golden-set evaluators before users feel drift.
- Trace IDs across LLM stacks for explainable debugging.
- Retention policies pruning stale chunks for compliance narratives.
How learning works
Short lessons and hands-on practice at your level—from fundamentals to tasks close to real work.
Getting started
Sign up, explore the interface, and take the skills check—we capture your baseline and starting point.
Personal plan
A path tailored to your chosen track: topics, module order, and practical assignments.
Theory and practice together
After each theory block—tasks at your level, from drills to connected work you can show employers.
Feedback
Automated checks where they fit; otherwise breakdowns of mistakes and hints for what to do next.
Your pace
Study anytime; revisit harder topics until you’re confident.
Close to the job
Focus on workflows and deliverables typical for the role—tasks, collaboration, and clear progress updates.
Ready to start learning?
Register on the platform and get a personalized learning plan for this profession.
Try for free. No card required.