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CRYDD

How to become a AI Engineer: the complete roadmap

AI engineers build software on top of existing AI models rather than training new ones. The work is integration and systems: wiring large language and vision models into applications, engineering prompts and retrieval, managing the data pipelines that feed them, and keeping the whole system fast and reliable in production. It is the fastest-growing on-ramp into AI, and it rewards software engineers directly.

Roughly 8–18 months from a software or data background; this path builds on existing development skill.

Stage_01

Developer and AI foundations

Skills

  • Strong programming — Python is the default
  • APIs and working with JSON
  • What models can and cannot do (AI literacy)

Project

Build a small app that calls a hosted AI model's API and does something genuinely useful with the result.

Stage_02

Building with models

Skills

  • Integrating LLMs and vision models
  • Prompt engineering
  • Embeddings and retrieval-augmented generation (RAG)
  • Vector databases

Project

Ship a retrieval-augmented assistant over your own documents — real integration, not a notebook demo.

Stage_03

Data pipelines and performance

Skills

  • Managing the data pipelines that feed models
  • Latency, cost, and system optimisation
  • Monitoring and reliability
  • Responsible AI in production

Project

Put your AI feature into a real system with a data pipeline behind it, then measure and cut its latency and cost.

Certifications

AI-102 anchors the applied-AI credential; a data-engineering credential proves you can feed and scale the system.

Where AI Engineers go next

  • Machine Learning Engineer
  • Software Engineer
  • Data Engineer