FieldsAI Engineer
AI Engineer
Build reliable applications on top of large language models.
Career pathMonths of workCapability pathWeeks of work
What Safua will teach
What you learn as an AI Engineer
- How language models work and where they fail
- Prompt design, retrieval, and tool use
- Evaluating and shipping model-backed features
- Cost, latency, and safety trade-offs in production
Example concepts
What you will understand
- Tokens, context windows, and embeddings
- Retrieval-augmented generation
- Evaluation and regression testing for non-deterministic systems
- Guardrails and prompt-injection defenses
Example practice
How you will practice
- Write and refine prompts against a held-out test set
- Wire a retrieval pipeline over a document set
- Build an evaluation harness that scores model output
Example projects
What you will build
- A question-answering assistant grounded in your own documents
- An evaluation suite that catches regressions across model versions
The proof you build
What a credential here means
Evidence that you can design, evaluate, and ship a model-backed feature with attention to correctness, cost, and safety.
Your work is observed with your consent, scored for independence and assistance, and turned into proof that carries a confidence level. The career path can reach a high-assurance credential, anchored by a scored capstone.
Start with AI Engineer
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