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  • Start any day: Day 1 opens when you enrol
  • 1–2 hrs a day, in your own time
  • Reviewed work and interview prep
  • Individual feedback every week
KLYMB.AI
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Career track 04This job is changing

Junior Developer, becoming AI Engineering Associate

Programming fundamentals, debugging, APIs, Git, frontend/backend basics and code quality.

Choose this trackSee the 30 days
AI skillsRAGAI agentsLLMOpsEvalsAI safetyGuardrails
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days
30days
checkpoints
4checkpoints
interview-prep days
2interview-prep days

What's changing in this role

Well-specified tickets are often first drafted by coding assistants. Teams value juniors who can review, debug and safely ship AI-assisted code.

What you carry over

  • + Reading and reasoning about code
  • + Fundamentals: data structures and debugging
  • + Learning fast

Who it is for

Built for people like you.

  • Computer science and IT graduates
  • Bootcamp learners who need real practice
  • Career switchers with basic coding knowledge

Skills covered

What you will practise.

  • JavaScript/TypeScript fundamentals
  • Debugging
  • REST APIs
  • Git and pull requests
  • Frontend and backend basics
  • Code review
  • Working with AI coding assistants
A team gathered around a laptop, talking through a piece of work

30-day overview

Every day, planned.

Four weekly phases of daily problems, an assessment after each week, and interview prep on Days 29–30. Open a week to see every day.

Enroll now
Week 1Days 1–7FoundationsFundamentals that still matterCore programming, Git and what junior work looks like after coding assistants.
  1. Day 1Reading unfamiliar code: Explain what a 200-line module does and where it could fail.
  2. Day 2Debugging a failing function: Find and fix a bug using a debugger, not guesswork.
  3. Day 3Git workflow: Branch, commit, rebase and open a clean pull request.
  4. Day 4Data structures in practice: Choose the right structure to fix a slow feature.
  5. Day 5Prompting a coding assistant: Get an assistant to write a function, then verify it.
  6. Day 6Reviewing a generated pull request: List every flaw in an AI-written PR.
  7. Day 7Checkpoint: PR reviewReview a generated pull request and list every flaw you find, with fixes.Practical task + short written reasoning, scored against a rubric
Week 2Days 8–14Practical executionAPIs and reviewing AI codeBuilding and consuming APIs, and spotting the failure modes of generated code.
  1. Day 8Consume a REST API: Fetch, display and handle errors from a public API.
  2. Day 9Build an API endpoint: Create a CRUD endpoint with validation.
  3. Day 10Silent bugs in AI code: Find the logic error that passes all tests.
  4. Day 11Security basics: Spot injection and auth flaws in generated code.
  5. Day 12Writing tests: Add unit tests that would have caught yesterday's bug.
  6. Day 13Refactoring: Improve a messy function without changing behaviour.
  7. Day 14Checkpoint: Planted defectsCatch the planted defects in an agent-written feature.Practical task + short written reasoning, scored against a rubric
Week 3Days 15–21Real-world problem solvingBuilding featuresFrontend and backend together, task decomposition and multi-step AI workflows.
  1. Day 15Frontend component: Build an accessible form component.
  2. Day 16Connecting frontend to backend: Wire a UI to your API with loading and error states.
  3. Day 17Task decomposition: Break a feature ticket into small, reviewable steps.
  4. Day 18Agent workflow on a repo: Ship a small feature using an AI agent, reviewing each step.
  5. Day 19Production bug: Debug an issue from a user report and logs.
  6. Day 20Code review etiquette: Give and respond to review comments professionally.
  7. Day 21Checkpoint: Feature deliveryDecompose and ship a feature through an AI-assisted workflow.Practical task + short written reasoning, scored against a rubric
Week 4Days 22–28Portfolio and interview readinessShipping safely and portfolioTests, CI, deployment and the practices seniors expect.
  1. Day 22CI pipeline: Run lint and tests automatically on pull requests.
  2. Day 23Deploying an app: Deploy your project and verify it works in production.
  3. Day 24Logging and monitoring: Add logs that make a production bug easy to find.
  4. Day 25Portfolio: README and decisions: Document how your project works and why.
  5. Day 26Portfolio: walkthrough: Present your project and code in 5 minutes.
  6. Day 27Interview drill: live coding: Solve a coding problem while explaining your thinking.
  7. Day 28Checkpoint: Ship reviewPut your feature through tests, CI and deployment, then present it.Practical task + short written reasoning, scored against a rubric

Weekly checkpoints

Four checkpoints. Real feedback.

Each one is marked on your reasoning, not just the output: and you defend it.

  1. 7After Day 7PR reviewReview a generated pull request and list every flaw you find, with fixes.
  2. 14After Day 14Planted defectsCatch the planted defects in an agent-written feature.
  3. 21After Day 21Feature deliveryDecompose and ship a feature through an AI-assisted workflow.
  4. 28After Day 28Ship reviewPut your feature through tests, CI and deployment, then present it.
A woman presenting her work to colleagues in a meeting room

Expected outcomes

What you will be able to do.

  • Debug systematically
  • Build and consume APIs
  • Use Git and pull requests professionally
  • Review AI-generated code for bugs and security issues
  • Explain your code in an interview

Role-specific projects

Evidence you can show.

  1. 01Full-stack app with API, tests and CI, deployed
  2. 02Documented review of AI-generated code with fixes

Interview preparation

Practise the questions Junior Developer interviews actually ask.

Preparation runs through Week 4, then the most-asked questions on Days 29–30. Here is what you will be asked, and what a strong answer looks like.

  • Days 29–30Interview prep after Week 4
  • Three kinds of questionTechnical, scenario, behavioural
  • Model answersWhat a strong answer shows
  1. 01Technical

    “What happens when you type a URL into a browser?”

    A strong answer shows

    DNS, TCP/TLS, HTTP request/response and rendering, at a sensible depth.

  2. 02Technical

    “How would you find the cause of a bug you cannot reproduce?”

    A strong answer shows

    Logs, hypotheses, narrowing down environment and data.

  3. 03Scenario

    “An AI assistant wrote code that passes tests but you are unsure it is correct.”

    A strong answer shows

    Reads it critically, adds edge-case tests, checks security.

  4. 04Technical

    “Explain the difference between PUT and PATCH.”

    A strong answer shows

    Accurate definitions with an example.

  5. 05Behavioural

    “Tell me about something you built and what you would change now.”

    A strong answer shows

    Specific decisions, trade-offs and honest reflection.

  6. Topics covered

    • Programming fundamentals
    • Data structures
    • Debugging
    • APIs and HTTP
    • Git
    • Code quality and testing
    Enroll now