4 Core AI Engineering Competencies Summarized by Andrew Ng

1/ After analyzing over 10,000 job postings and interviewing dozens of AI experts, hiring managers, and recruiters, Andrew Ng identified the four most critical AI engineering competencies:

Building and deploying AI applications, software engineering fundamentals, using Coding Agents, and defining what to build.

Writing code is just the starting point.

2/ He emphasizes that this map discusses AI engineering competencies, not just the role of an “AI Engineer.”

Just as every developer needs to understand cloud computing, they aren’t necessarily called cloud engineers.

In the future, whether you are a full-stack, data, DevOps, or machine learning engineer, you will need to master these skills.

3/ The first competency: Building and deploying AI applications.

Traditional software produces relatively deterministic outputs, but AI applications cannot guarantee the same result every time.

Therefore, beyond LLMs, RAG, context engineering, and agent workflows, it’s more important to learn evaluation and error analysis, using statistical methods to continuously measure, guide, and constrain the system.

4/ The second competency: Software engineering fundamentals.

AI can generate code, but it doesn’t automatically weigh trade-offs in cost, scalability, speed, reliability, security, and privacy for you.

The stronger your fundamentals, the better you know what context to provide to a Coding Agent, and the better you can catch the bad decisions it might make. Vibe Coding cannot replace engineering judgment.

5/ The third competency: Using Coding Agents.

True capability isn’t just about writing prompts, but knowing when to intervene and when to step back, how to manage context, and how to balance planning with execution.

It also involves writing clear specifications, providing validators and evaluations, enabling the agent to close its own feedback loop, and coordinating multiple agents to work together when needed.

6/ The fourth competency: Defining what to build.

As Coding Agents become better at delivering according to specifications, the engineer’s work moves upward: understanding the business, the customer, and the product goals, and participating in defining the specifications themselves.

Knowing when to quickly build an MVP for user testing and when to slow down and polish will also become increasingly important.

7/ Underpinning this competency map is one common requirement: continuous learning.

Tools, models, and best practices are still evolving rapidly. An agent workflow that works today might need to be rebuilt in a few months.

The scarcest engineers of the future will be those who combine technical judgment with the ability to proactively identify problems using AI and drive product implementation.

Read the original article:

Anthropic wanted to test if Claude could successfully design new protein binders from scratch, known as de novo design.

With a protein design prompt written by a human expert, Claude autonomously designed binders for 14 out of 15 targets.

Subsequently, Anthropic partnered with Adaptyv Bio and Twist Bioscience to have them independently build and test the proteins designed by Claude.