Role

AI Platform Engineer

Builds the shared internal tools and systems other engineers use to ship AI features.

14 chaptersAbout 5–6 months15–30 min a day14 skills2 projects
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Day 1 on the AI Platform Engineer path.

What a AI Platform Engineer does

Builds the internal platform that lets a whole company use artificial intelligence (AI) safely and fast. It produces shared tools: a gateway to large language models (LLMs), evaluation systems, and search pipelines. Other engineers then ship AI features without rebuilding the basics each time.

  • Build a gateway that routes requests to many large language models (LLMs)
  • Create tools to store, version, and test prompts across teams
  • Set up evaluation pipelines that score model output for quality
  • Run a vector database (one that finds text with similar meaning) for fast search
  • Build retrieval-augmented generation (RAG) pipelines that feed models trusted data
  • Add guardrails that block unsafe, leaking, or wrong AI responses
  • Track cost, speed, and errors so teams can debug AI features
  • Document the platform and support engineers who build on it

A day in the life

  1. Check dashboards for model errors, cost spikes, and slow requests
  2. Plan platform work in a short sync, then share changes with the engineering team
  3. Build or review a feature for the LLM gateway or evaluation tools
  4. Help a product team debug a flaky retrieval-augmented generation (RAG) pipeline
  5. Test new prompt versions against a saved set of scored examples
  6. Tune guardrails after a model returns an unsafe or wrong answer

Tools you will use

Languages: Python, TypeScript, GoModel gateways: LiteLLM, OpenRouter, cloud model APIsOrchestration: LangChain, LlamaIndexVector databases: Pinecone, Weaviate, pgvectorEvaluation: LangSmith, Ragas, custom test setsPrompt tools: PromptLayer, Git for versioningInfra and cloud: Docker, Kubernetes, AWS, Azure, Google CloudObservability: Grafana, Prometheus, tracing

Your plan

Chapter by chapter.

1~2 wks

See the work of an AI platform engineer

StartFree
2~2 wks

LLM foundations and prompting

Skills
3~2 wks

Architecture write-up: explain a system you built

Proof
4~2 wks

RAG and retrieval pipelines

Skills
51–2 wks

Meet people doing the work

People
6~2 wks

Evaluation, safety, and guardrails

Skills
7~2 wks

Programming, APIs and cloud services

Skills
8~2 wks

System design and security basics

Skills
9~2 wks

Problem solving and collaboration

Skills
10~2 wks

Adapting when things change

Skills
11~2 wks

Build an AI feature: a RAG app, an LLM gateway, or a guardrail layer

Proof
121–2 wks

Prepare for AI platform engineer interviews

Interview
131–2 wks

Choose your route into AI platform engineer work

Decide
14on your timeline

Apply for AI platform engineer roles

Apply

By the last chapter

This is what you can show.

Things you've made

Architecture write-up: explain a system you built and Build an AI feature: a RAG app, an LLM gateway, or a guardrail layer

Skills you can prove

14 skills, each rated on work you actually did.

People you've talked to

4 people who do the job, with a message ready for each.

Questions you can answer

18 interview questions and a mock interview, with feedback.

Credentials

8 credentials compared, so you can pick one, or decide you don't need one. None is required.

Ways in

There is more than one route.

Ways to study

  • Bachelor degree in computer science, software engineering, or related field
  • Strong software engineering background plus self-study in machine learning
  • Online courses in large language models, retrieval, and platform engineering
  • Several years of backend or platform work before moving into AI tooling
  • Master degree in machine learning or data engineering for deeper theory

How people get their first job

  • Build a backend or platform engineering base first, then add AI projects
  • Ship a small retrieval-augmented generation (RAG) app with tests and clear docs
  • Contribute to open-source large language model (LLM) tools and pipelines
  • Move from backend or DevOps work into a team that builds AI tooling
  • Run an evaluation harness for a model and publish the results as a portfolio piece

Life skills on this path: Networking · Job interviews · Job search

How the work is changing

What AI is doing to this role

How AI is changing this role · one of 6 tasks we track

Build the large language model gateway

Sped up a lot

What AI does

AI drafts the routing code and client logic for a gateway across many models.

Still yours

You design how it fails over, caps cost, and handles many teams at once.

Reviewed September 2026

In the app · Premium

The rest is in the app

  • How AI affects the other 5 tasks
  • Whether this job is growing or shrinking
  • How hard the first job is to get
  • Similar roles that are easier to get into
  • Updated every month, with sources
See it in the app →

How we rate a job →

Already working

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Questions

Questions about this path

How long does it take to become a ai platform engineer with Welica?

The path is 14 chapters, 5–6 months at 15 to 30 minutes a day. You can go faster or slower; the plan moves with you.

Do I need a degree?

Not always. Common routes are bachelor degree in computer science, software engineering, or related field, strong software engineering background plus self-study in machine learning, online courses in large language models, retrieval, and platform engineering, several years of backend or platform work before moving into AI tooling, or master degree in machine learning or data engineering for deeper theory.

What is free?

Chapter 1, See the work of an AI platform engineer, is free for good. Premium unlocks the rest of the path.

Start the AI Platform Engineer path.

Chapter 1 free. About 15 to 30 minutes a day.

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