Foundation
- Python or TypeScript, HTTP APIs, JSON, Git and testing.
- Tokens, context, embeddings, sampling and model limitations.
- Baseline task design and labelled examples before frameworks.
Build Intelligence · Career guide
AI Engineering
Build dependable product features around existing AI models, with retrieval, evaluation, cost, latency and safety treated as engineering constraints.
Career snapshot
A work-pressure profile, not a personality score
Three dots never means ‘better.’ It only means this dimension appears more often in the work.
Coding
Mathematics
Systems depth
Investigation
Creativity / design
Communication
Production responsibility
What would I actually do?
The work behind the title
- Translate a user task into an evaluable AI feature.
- Design prompts, retrieval, tools and structured outputs.
- Build representative evaluation sets and regression checks.
- Measure quality, latency, cost, privacy and failure modes.
- Monitor production behavior and create safe fallback paths.
Reality check
Finishing a tutorial and owning production consequences are different skills. The point is to see the gap, not hide it with hype.
You might enjoy this if…
Signals worth noticing
- You like turning an impressive demo into a repeatable system.
- You enjoy evaluating ambiguous outputs with explicit evidence.
- You care about backend reliability as much as model capability.
You might dislike this if…
The uncomfortable part
- You only enjoy trying new models, not testing or maintaining them.
- Probabilistic output and changing provider behavior feel intolerable.
- You want one prompt to replace product discovery and system design.
A realistic day
No day is identical, but the pressure may feel like this
This is a plausible mix of recurring tasks, not a scripted career fantasy.
Review a failed answer against the evaluation set.
Improve retrieval and compare quality, cost and latency.
Add a regression case and review a guarded rollout.
What should I learn?
A layered sequence, not a technology dump
Core
- Structured outputs, retrieval, tool boundaries and citations.
- Evaluation sets, quality metrics and failure taxonomies.
- Cost, latency, privacy, prompt injection and fallback behavior.
Practical
- Build a bounded assistant over documents you own.
- Compare it with a simple keyword baseline on labelled questions.
- Add regression checks, source display and an honest refusal path.
Later
- Hybrid retrieval, reranking and agent/tool evaluation.
- Observability, caching, routing and controlled rollout.
- Fine-tuning or self-hosting only when evidence justifies it.
What not to learn yet
Not yet—because sequence matters
- Agents before a single-step baseline and evaluation set.
- Uploading private data to an unapproved provider.
- Claiming production quality from a few hand-picked demos.
Future You says
‘Later’ does not mean useless. It means you will learn it faster once you have a real problem for it.
AI & this career
Task Automation Exposure
This is not a replacement probability. Capabilities will change, so separate tasks, judgment and responsibility.
Prompt and integration scaffolding
Models can draft the very code and prompts used to call them, but generated patterns are often untested or provider-specific.
Student response: Keep small baselines, typed contracts and regression tests around every generated change.
Failure analysis and evaluation design
AI can propose cases and judges, but model-generated evaluation can share the same blind spots as the system.
Student response: Use human-labelled representative cases, deterministic checks and explicit uncertainty.
Risk, product boundaries and release accountability
Someone must decide when the feature should refuse, escalate or not be built.
Student response: Practice threat modelling, privacy review, staged rollout and rollback decisions.
Curated learning resources
Learn → Practice → Go deeper
Resource count is not progress. Finish the recommended scope and build something.
LLM Course
A free technical foundation that makes model behavior less mysterious.
Do this much: Study model and transformer foundations before using the later application chapters selectively.
Last reviewed:
Full Stack Deep Learning
Connects models to the engineering work required around production systems.
Do this much: Use selected lectures on development infrastructure, testing, deployment and continual learning; verify current tool details separately.
Last reviewed:
Made With ML
Treats evaluation and reliable delivery as part of ML/AI engineering.
Do this much: Focus on data, evaluation, testing and production lessons; reproduce a small example rather than copying the full stack.
Last reviewed:
Next choices
What evidence will you gather next?
Nearby directions
From skills to a destination
Where could you take this?
The career roadmap explains what to learn. A goal guide helps you put those skills to work. Start with one—not every route at once.