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AI Engineer / LLMOps

Build production-grade AI applications

RAG systems, prompt engineering, vector DBs, agentic workflows, evaluation, AI safety. The hottest job market for 2026 — every company wants to ship AI features but few engineers know how to do it production-grade.

Claude APIOpenAI APILangGraphVector DBs (Pinecone, pgvector)PythonFastAPIEmbeddingsPrompt engineering

Salary range

$110k – $220k

entry → mid US

Time to complete

12 wk

24 wk part-time

Lessons

14

4 phases

Capstone

Yes

real cloud account

Salary range is sourced from Levels.fyi 2026 AI-engineer postings, US tech-hub adjusted. Your specific outcome depends on location, experience, interview performance, and market conditions.

Roles this path prepares you for

  • AI Engineer
  • ML Engineer (LLM-focused)
  • Applied AI Researcher
  • AI Platform Engineer

Curriculum

1

LLM Foundations

Free preview

What an AI engineer does, how LLMs work, prompt engineering, embeddings.

4 lessons

2

Building RAG Systems

Locked

RAG fundamentals + improvements, evaluation, fine-tuning, vector databases, AI product design.

6 lessons

3

Agentic Workflows

Locked

Agents and tools, safety and prompt injection, cost and latency.

3 lessons

4

Career Launch (AI)

Locked

AI engineer portfolio, interviews, and career path guidance.

1 lessons

Capstone project + completion certificate

Production RAG system for a fictional knowledge base

Ingest a 500-document corpus, build a chunking + embedding pipeline, deploy a retrieval API, add an agentic frontend with tool use, ship an evaluation harness with quality metrics.

Deliverables

  • ·Working RAG system deployed (FastAPI + vector DB)
  • ·Evaluation harness with retrieval + answer-quality metrics
  • ·Agent with at least 3 tools working end-to-end
  • ·Cost tracking + latency dashboard
Completion certificate. Finish every lesson in this path and we auto-issue a CloudPath AI Engineer / LLMOps certificate to your account, with a shareable verification URL. Show it on LinkedIn, your portfolio, or in a recruiter conversation.

Before you start

  • ·Comfortable with Python
  • ·Cloud Foundations or equivalent (need to deploy things)

What you walk out with

  • Build production-grade LLM applications end-to-end
  • Design + measure RAG system quality
  • Interview for AI Engineer roles confidently

Preview — opening soon

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