How to become an AI Engineer

I’m unsure if you have a problem explaining AI, but I do.

Thus, an AI product is more than just a model; it combines AI technology with software engineering, design, and business processes to deliver a practical solution that solves real-world problems. We also need to consider data and people.

I reposted this post on LinkedIn because I like the definition, and I believe it is a great resource to explain AI.

https://www.linkedin.com/posts/ken-sinclair-8246965_good-read-for-automatedbuildingscom-followers-activity-7283151958318751744-BUNA?utm_source=share&utm_medium=member_desktop

This is a good read and resource. We all need to be AI engineers

It starts

This term, AI Engineer, is a complex term. One well known book defines an AI Engineer as developing using LLMs. In my view, this definition is limiting.

I define an AI engineer more broadly covering three aspects:

Developing AI systems with the help of LLMs(prompt engineering, RAG and fine tuning)
LLM assisted code development (Claude, OpenAI, Github copilot etc)
Machine learning and deep learning based systems that are not based on LLMs

This is a complex but pragmatic definition – especially for people who want to transition their career to AI.

What is an AI product
An AI product is a software application or system that leverages artificial intelligence (AI) techniques to solve problems, improve processes, or enhance user experiences. It typically integrates AI models, algorithms, or systems into a functional solution that delivers value to end-users.

Components of an AI Product
AI Models: Algorithms trained on data to perform specific tasks.
Data Infrastructure: Systems to collect, process, and store data.
User Interface (UI): Frontend through which users interact with the product.
API or Middleware: Connects the AI component with the application layer.
Deployment Pipeline: Ensures efficient rollout and updates of the AI model.

Read the online post and follow the links