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AI engineer skills checklist and self-assessment

Assess 20 practical AI engineering skills across software, AI applications, retrieval, agents, evaluation, and operations. Get a private, saved-on-device learning plan.

Gajendra RaiUpdated October 11, 202612 minBeginner

Interactive self-assessment

Rate your AI engineering skills

Work through five practical skill areas. Your private results point to learning resources, not a pass/fail judgment.

0 of 20 skills rated

Choose the highest level you can support with a real example.

Answers are saved only in this browser on this device. They are not sent to Migo Mind.

Checking for saved answers…

Not yet
I have not practiced this skill.
Learning
I can explain it or follow a tutorial.
With guidance
I can complete it with references or review.
Independently
I can deliver, test, and explain the work.

Software foundations

0/4 rated

Build and test small, maintainable services.

I can build a small application that calls an API or model SDK.
I can validate inputs and model outputs against a defined schema.
I can write tests for normal cases, invalid inputs, and failures.
I can handle timeouts, retries, errors, and configuration safely.

AI application design

0/4 rated

Shape model behavior into a useful application workflow.

I can explain the limits of a model-powered feature to a user or teammate.
I can design a prompt and output contract for a specific task.
I can test how an application responds to missing or conflicting input.
I can separate trusted instructions from user-provided content.

Data and retrieval

0/4 rated

Prepare sources and check whether the right evidence is found.

I can prepare a small document collection for search or retrieval.
I can inspect whether retrieval returns the expected source passages.
I can make generated answers refer to evidence that can be checked.
I can identify data-access, freshness, and deletion concerns in a workflow.

Agents and safety

0/4 rated

Constrain tools and define safe behavior when things go wrong.

I can give an agent only the tools and permissions it needs.
I can validate tool arguments and handle tool errors or timeouts.
I can identify when a person should approve or take over a workflow.
I can test how untrusted content might influence instructions or tool use.

Evaluation and operations

0/4 rated

Measure quality and operate a system after it is built.

I can create representative examples to evaluate a change before release.
I can track useful signals such as failures, latency, and usage cost.
I can explain a quality, reliability, security, or cost trade-off I made.
I can document a limitation and a practical fallback or escalation path.

How to use this AI engineer skills checklist

Rate each statement based on work you can demonstrate today, not what you have watched or read. Choose the strongest level you can support with an example, and leave a prompt unanswered if you are not sure yet. Your progress is stored in this browser on this device; it is not sent to Migo Mind.

  • Not yet: I have not practiced this skill
  • Learning: I can explain the idea or follow a tutorial
  • With guidance: I can complete a task with references or review
  • Independently: I can deliver, test, and explain the work

What the skill areas cover

AI engineering combines application development with data, model behavior, and production responsibility. This checklist groups related work so you can spot a useful next step instead of treating every new framework as a separate career requirement.

  • Software foundations: APIs, Python, tests, and error handling
  • AI application design: model behavior, output contracts, and prompt boundaries
  • Data and retrieval: source preparation, retrieval checks, and grounded responses
  • Agents and safety: limited tools, approval points, and untrusted inputs
  • Evaluation and operations: repeatable quality checks, monitoring, and trade-offs

Read your results as a learning signal

The totals summarize your own ratings. They are not a validated competency model, certification score, or prediction of whether an employer will hire you. A low category points to practice; a high category is a cue to gather stronger evidence, such as a tested project, design note, or measured result.

  • Choose one lower-rated skill and build a small working example
  • Write down the test that would show whether it works
  • Ask a peer to review the result and note what changed
  • Repeat the checklist after you have new evidence

Turn a skill into portfolio evidence

A portfolio is more persuasive when it shows decisions and results, not only a screenshot of a chat interface. Pick one real or clearly fictional user problem, keep the scope small, and document the behavior that still fails.

  • State the user need and success criteria
  • Show the application and data flow, including identity boundaries
  • Include representative tests and evaluation results
  • Explain quality, latency, reliability, and cost trade-offs
  • List limitations and the next improvement you would make

Choose a focused next step

Use the category suggestions in your results to open a relevant learning path. If several areas need work, start with the foundation that blocks the others: application basics before complex orchestration, and retrieval measurement before judging generated answers. Keep one small project as the thread that connects each new skill.

Choose your next step

Use your ratings as a starting point, not a final judgment. Pick the next action that produces evidence.

Learn

Start with AI foundations

Build an understanding of AI applications, model behavior, and responsible implementation.

Open resource Build

Plan a role-focused project

Turn a target role into a twelve-week learning and portfolio plan.

Open resource Practice

Explore interview questions

Use role-based technical questions to explain decisions and trade-offs aloud.

Open resource

Frequently asked questions

What skills does an AI engineer need?+

The mix varies by role, but practical foundations often include application development, APIs, model behavior, data handling, evaluation, security, and operations. The checklist organizes these areas to help plan learning; it is not a universal job specification.

Is this AI engineer assessment a certification or hiring test?+

No. It is an informal self-reflection and study-planning tool created by Migo Mind. It has not been validated as a certification, hiring screen, or predictor of job performance.

Are my answers uploaded or shared?+

No. The answers are saved in local storage in the browser on the device you are using. They are not submitted to Migo Mind. Clearing this browser's site data removes them.

How should I improve a low-rated skill?+

Choose one small task that exercises the skill, define a checkable outcome, build it, and document what happened. For example, add schema validation and failure tests to a model-backed API, then ask someone to review the result.

Official sources

These official Microsoft Learn resources provide background for the technical areas in this checklist.

Put this into practice

Build a practical foundation and turn your next learning step into working evidence.

Open resource

Keep building

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