Recently, I have been involved in numerous aspects of the engineering hiring process. I have noticed some phenomena and some changes that would be of value to the global engineer. So, in this issue, I am going to share with you what I have learned from my experience in hopes it helps you progress your career more.
Applicant Tracking System (ATS) really do mean there is a vital part of your resume
When I reviewed the resumes of applicants through the ATS (a program designed to make the hiring process easier) for a role I was wishing to fill, I noticed that each time I clicked on the applicant’s link, the ATS showed a preview of the top 1/3 of the first page of the resume. The end result was that I automatically used this to do an initial screening. Therefore, you should ensure that this part of your resume has a summary of your skills and what you have to offer. If a resume had a summary in this section and that summary aligned with what I was looking for, then I was more likely to pass it to the next stage.
Portfolios are great for engineering roles
 Portfolios are usually for roles like industrial design, graphic design, fashion design and other roles where you can assess the skill of the applicant by what they share.
However, in the engineering context, a section that was dedicated to the various achievements that the applicant was most proud of helped phenomenally. An image was good, if confidentiality allowed, but otherwise a descriptive heading so the hiring manager could grasp the nature of the challenge was a great start. Then a description of the nuances and how the challenge was completed.
I didn’t care how much longer it made the resume; this was excellent insight to assess an applicant.
Photos are good in the right context
I recall one applicant who had a photo of himself on the front page of the resume. He just looked like a design engineer. The rest of the team agreed that after seeing his photo, they could easily imagine him sitting in the same department next to the others in the team.
I suspect the photo was taken by a professional who asked him the role and industry he was interested in, then took care of the rest. He had the right clothes, worn the right way and he had an expression that conveyed that mix of creativity and practicality with a strong dose of cooperation that you want in all members of an engineering design team.
There is no substitution for proper assessment of skills in the interview
Many hiring managers still seem to think that they can come up with the ideal questions to assess skill. In my experience, they can’t. I think half the reason is that they don’t actually think about the skills they really need, and instead focus on specific domain knowledge.
I have found that the best way to assess an applicant is by giving them:

• some pencil and paper,
• a relevant challenge described on another sheet of paper, and
•  an hour or more to explore and solve (if possible) that challenge.

You get to see their knowledge, the way they think, the way they formulate new questions for better understanding, how well they communicate with words and pictures, and their comfort with this type of work.
It would be best if you get challenged like this in an interview, but, the fact is, you will likely need to show domain knowledge for the respective role. And it’s hard to say which aspect the interviewer will latch on to – sometimes they ask you about a specific challenge they had the prior week which is not even indicative of their usual work.
AI was a huge help
I used AI in a number of ways:

1. I used it to screen the resumes and rank them based on the attributes I wanted. The great thing about this, is that it read the entire resume looking for evidence of the attributes I was after. Thus, it would find things I would miss – making it fairer on the applicants. Because it also explained why it ranked each applicant the way it did, I was able to refine the prompt if needed and I felt very confident in the findings. This is ideal when you have 100s of applicants – especially if they have extra sections like portfolios in their resumes.
2. Once we had culled the applicants down to about 30, the HR support team was then able to call those 30 for a prelim interview. Because they dictated the answers – likely using AI – we have complete text of their responses. By ensuring the questions asked were aligned with the attributes we were after (things like what’s your favourite engineering design tool or philosophy and why) each answer revealed a lot about their experience and practice. Again, these answers could be put into AI for quick assessment.
3. Developing questions. AI provided great feedback to the questions and assessments I had conceived. It helped me refine them to ensure that there was a minimised chance of misunderstanding and that they indeed assessed for the desired attributes. It therefore helped remove unconscious bias.

I can see it only doing more in the future.
Summary of key points for the global engineer

1. Make sure you have a clear expression of yourself and what you offer in the top third of the first page of your resume.
2. If you are going to do a photo, then consult a professional photographer who understands the goal and purpose of the photo.
3. Consider adding a portfolio (or any other supporting sections). This helps an interested engineering hiring manager learn more about you once they show interest in your application. It also works well with AI systems that can digest a lot of information.
4. Soon AI will become more common, and I can see it making things fairer, but until then, you do need to be ready for idiosyncratic domain-specific questions from employers.

If you have been paying attention, then you have heard about the USD 12 Billion that Jeff Bezos and Vik Bajaj raised to develop an AI general engineer. One that can do what any other engineer can do – so it should be much like a global engineer. Read more here if you are not aware.
If possible, then what would that mean for professional engineers like us?
Bezos thinks this will help augment engineers and give them more time. Others think that all engineers will eventually be replaced. And what has been achieved thus far is not yet in the public domain.
In this article, I am not going to talk specifically about what Prometheus will or will not be able to do. Instead, I am going to use this as an opportunity to talk about the following:

1. What would be needed to train such an AI system?
2. What that means for the nature of such an AI system?
3. What would that mean for engineers working with such an AI system?
4. Should we even expect such an AI system to think the same way as human engineers?

I am not going to assume that I will know exactly what is being done on Project Prometheus. Nor that I will have the exact answers, but this conversation will help you and me think more about what’s needed to make an AI engineer and what it is to be an engineer in general.
What would be needed to train an AI engineer?
In my book on being a global engineer I noted what has been found about the way the best engineers think. In summary, they do 3 things really well:

1. They frame problems in a way that makes them easier to solve. They do not accept the problem as is. They look at it from different angles to find another way to define it – and then find an angle that makes the solution obvious and easier to implement.
2. They think systemically. They do not focus on just the challenge in front of them. They look at all peripheral elements for both challenges in accomplishing their goal and opportunities to help achieve those goals.
3. They use first principles. They do not make arbitrary decisions about the values of key parameters. They calculate the optimum value based on theory. They also use theory to understand the nature of any challenge they face.

Would an AI engineer explicitly be trained on these three attributes?
If so, then would we explicitly state these as needs or would we train the system to engage in these actions by default?
Not only that, but would we consider other, very human aspect of engineers?
One example is fixation. Where someone, in this case an engineer, stays focused on an idea that has come to them. It might be because it is the first one that showed promise, something they are excited about, or something that seems obvious because they have been working in a certain field for so long.
Fixation can, at first, seem like a bad thing. However, it has also been found to provide drive – and engineers have developed remarkable innovations by overcoming the challenges caused by this fixation. Sometimes better than what would be expected by someone objective who could see that fixation taking effect.
Would we want the AI engineer to show this tendency toward fixation to explore ideas fully?
Which brings us to another attribute: co-evolution
Many engineers talk about the need to iterate a design. I agree with the need, but I do not think the word “iterate” explains the depth of what is going on. The word “co-evolution”, I think, elicits the true deeper meaning of what is happening. As a solution is implemented in some sort of trial, new information is generated about the problem. So as we generate the solution, we better understand the problem. The solution and the problem evolve together.
As we co-evolve both the solution and the problem, we expand our understanding of the solution and problem space. It is like exploring a new land – but one where the terrain is the challenge we are solving. Some solutions find a path through rocky terrain that opens new areas to explore that we never knew about.
Do we need to find a way to train an AI engineer to explore like this?
If so, then is it all through simulation or do we need to run the physical tests under its instructions and then report back (expecting questions about how well we ran the tests)?
What does this mean for the nature of such an AI system?
Considering the above, do you feel that there is data enough to train an AI system to be a general engineer?
Personally, while I do think AI engineers will eventually be a thing – and eventually be better than us, I am not sure it will happen as a result of ingesting large amounts of data.
I think there will at least need to be some kind of reinforcement learning. Where the AI will adjust through trial and error to provide better outcomes.
That means the system will need some kind of objective measure of success. It will need to be able to assess how well an idea has performed. Maybe based on a numerical goal. Maybe on some other system that is more “reflective” so it can ponder how it could have done better – comparing what it actually did with other things it could have done.
This is something that many engineers do. You have possibly thought back to things you did years ago and thought “now why didn’t I do it like this?” I know I have done that.
So the AI engineer would need to have some kind of inner monologue. Is that possible?
Maybe it will actually need to be a team of AI engineers – each with different parameters from their own training – interacting with each other. Comparing the performance of each other’s ideas and then adjusting their own parameters to think in a better way.
So it might be that we never have a single AI engineer in the true sense, but a collection of AI engineers working together and then presenting output as if it is from a single AI engineer.
This considers the “cognitive” aspects. But what about the physical or real-world execution?

• Will the AI engineer call up suppliers to confirm current capabilities and use that as an input?
• Will it have hyper-generalised transducer (maybe a humanoid robot) that allows it to build and run prototype tests?
• It will likely be able to connect to rapid prototype machines, but will humans (with their nimble hands) still be the ones putting the parts together and finding (with their comprehensive senses) the ideal place to run the assembled product?
• What is the most economic execution of the above actions at this time, and what does that mean the AI engineer should be like?

The above does seem to point to the notion put forward by Bezos that the generalised AI engineer will be working with other engineers to increase productivity and free up the time for humans. For now, at least.
What does this mean for engineers?
If you do ever find yourself working with a general AI engineer, then it would seem likely that you will be using it to do the things that would normally be time consuming for you. Explore numerous ideas, source the appropriate first principles, conduct simulations and calculations, generate test plans, come up with questions to better refine the problem statement.
You could then share your own ideas to help move things along.
At first that seems like you get to enjoy the fun parts more. And have more free time.
 
However, in the continued pursuit of profits, I am sure there would eventually be fewer engineers working for the same amount of time. History just shows that’s how things go.
But how will you interact with the AI engineer?
Again, in my book on being a global engineer I covered how words are not enough to explain engineering concepts. How engineering is very much a visual thing – even though other senses can help better understand any challenge you are facing. How do you convey what’s in your mind’s eye to the AI engineer?
Engineers are either going to have to improve their CAD skills to quickly convey their ideas or work on their ability to sketch. You might be able to use words to ask for an initial concept, but sooner or later you will need to edit some sort of visual to convey what’s in your mind.
But will the AI engineer visualise the way we do?
Should we even expect such an AI system to think the same way as human engineers?
We have developed our engineering skills by working with the brain that biological evolution gave us. This does not mean it is the only way invention can come about.
There are numerous examples in nature of animals showing the action of invention. Sometimes it is thought to be a result of instinct from evolution and sometimes from actual intent. But, as we learn more, the definition of intelligence and creativity seems to broaden.
Such ideas have also been explored in literature.
In the Children of Time series, Adrian Tchaicovsky explores how creatures with multiple brains or limited memory capacity could evolve inventiveness. In some, there is a separate brain that takes the problem as given by the main brain and then returns a solution some time after processing. Others have an ability to install and uninstall knowledge so they can use what is needed for the respective task.
In the West of Eden series, Harry Harrisson explores the nature of intelligence and inventiveness of an evolved reptilian brain. Where all technological advancement is an iteration on previous efforts with no major leaps – keeping them away from metal working and machinery, but phenomenal selective breeding.
Given the diversity of thought that we have found in nature and what we can conceive when we put the effort in, we can expect the possibility of a general AI engineer that thinks very differently from how we do while still generating excellent solutions.
Will we be able to work with such an engineer? Will we automatically think it inferior? Will we try emulating it ourselves after it reveals new ways of thinking to solve engineering problems?
In this article, I have only covered the major and some select aspects of engineering cognition. I have also only covered a select number of engineering activities associated with the full implementation of any engineering solution. I have also only alluded to the importance of engineering teams. And the diverse nature of the potential forms of intelligence was given only a cursory coverage. To deal with them properly would require an entire book.
That means there is much more to explore and consider. I currently can’t make any solid predictions about how this will all end up.
However, I do now feel convinced that the effort to make this general AI engineer will reveal much more about what it is to be an engineer. Even if it fails. So I plan on following it in detail, and I think you should too.
As you think about it now, what challenges and opportunities do you see? What would you want a general AI engineer to be like?
Share your thoughts – this is a conversation I would like to extend so we can all learn more about what it is to be an engineer.

As an engineer, you probably don’t like the documentation side of things. You should at least see its value, but, in my experience, engineers are usually not fans.
We know that it protects the organisation, supports future work, and helps others understand what we have done. And yet, under time pressure, documentation is often the first thing to be condensed, deferred, or quietly abandoned.
This is not new.
But there is going to be a new pressure to create even more documentation.
Not because management suddenly cares more. Not because standards have become dramatically stricter.
But because the value of documentation has fundamentally changed. And it’s because of AI.
How AI Will Make Documentation More Valuable
Historically, documentation has been hard to use well.
It was often written because it needed to be and then just left. People might refer to meeting minutes to double-check their deliverables. But usually, it would be left until there was an audit or something official like that.
And that meant it was written in a way that was only useful for such things. Which in turn meant that it was hard to use for other things. Things like:

• Understanding why an engineering system was set up the way it is. Ideal for new members of an engineering team.
• Knowing if options had been considered or tried – and if they would work or not.
• Explaining how the system works – to engineers and non-engineers.

This was, and is, a great loss. People could easily make the same mistakes or not understand the system well enough to think of improvements.
AI changes that completely.
With comprehensive documentation of an engineering project (design decisions, options considered, trade studies, constraints, assumptions, experiments tried, and outcomes), it is possible to interrogate all of it quickly.
Not by reading everything line by line, but by asking the question you want answered.
Interrogating Engineering History
Imagine you are looking at an existing engineering system and considering a change.
If you have good documentation, you can now ask an AI system questions like:

• Was this kind of change considered previously?
• What alternatives were explored at the time?
• Was something similar attempted and found to fail?
• What constraints drove the original decision?
• What assumptions were critical—and are they still valid?

The AI does not invent the answers. It mines your own engineering record.
This documentation becomes your operational knowledge.
You won’t repeat the same mistakes and you can better assess new ideas with the knowledge you now have. It’s like you have become the old guy in the company who has seen it all!
Documentation Will Be Demanded More – Because It’s Easier
There is definitely a certain irony here.
The same technology that makes documentation more valuable also makes it easier to produce. Engineers can now:

• Dictate notes instead of typing them
• Generate concise summaries from long discussions and disjointed meetings
• Convert rough thoughts into structured explanations
• Maintain logs with minimal friction

As I mentioned in the previous article on one-pagers, AI can help you turn raw notes into something readable and useful. That capability will remove many of the traditional excuses for under-documentation.
And once that happens, the expectation will shift.
If documentation is easy and highly valuable, it becomes harder to justify not doing it.
For the Global Engineer (and Company)
This matters even more in a global engineering context.
When documentation exists, AI allows it to be repurposed for different audiences.
Language barriers are reduced. Differences in writing style, cultural expectations, and technical depth can be adapted on demand.
That means documentation no longer has to be perfect for everyone. It just has to exist. Making it easier again for an engineer to work anywhere in the world.
It also means an engineering company can be more robust.
Engineering teams change – sometimes when you least expect it.
People get reassigned. Projects ramp down. An entire engineering team gets taken out by food poisoning at a corporate barbecue.
When a new team comes in, good documentation plus AI dramatically reduces the recovery time. New engineers can interrogate the history of the project instead of starting with fragments and assumptions.
Think about all the lunar exploration knowledge that needs to be relearned with the recent efforts to return to the Moon.
Knowledge that can be reused, transferred, explained, and interrogated is far more valuable than knowledge locked inside a few people’s heads. When all past experience within a company is documented and easy to use, it increases the value of the company’s intellectual property by orders of magnitude.
And an engineering firm would be foolish not to demand all knowledge now be documented.
The Shift That’s Coming
Engineers are probably going to be asked to document more than they ever have before.
You might not like it. You might resist. But, as the above shows, the payoff will be real.
The best you can do now is start using AI to make this documentation easier to generate and then be mindful to use AI to access that documentation (and others) in the future.

I know I am not the first person to take on the topic of AI of late. But the conversation on how it could affect engineering in the broadest sense has not, as far as I have witnessed, been explored as much as it could be. In this post, I am going to go over some recent experiences I have had with AI in engineering, and then, from that, talk about what we could expect.

Can AI do engineering?
Recently I have been developing an AI agent to help people like you think of ways they can be better engineers. I have trained it on the knowledge I have documented on best engineering practice and given examples of how to reply in certain cases. 

The agent is called Ingeny. Try it out here.

This is the first version, and it is still evolving. So if you take the time to explore it to see how it can help you with things like your engineering skill development, career progression and working with other engineers, then you will help make it better. I would appreciate you taking the time to help evolve it. By the way, the plan is to keep it free so engineers can always use it as they wish and need.

In the process of training Ingeny, I needed to train it to respond differently depending upon whether questions are about improving engineering expertise or about actually doing engineering. In the former it does not need to provide a warning that it is not a qualified engineer. In the latter, it should let you know that while it has offered as much insight as it can, into say a design for EMC, it is still up to you to do the engineering work. 

This proved to be demanding. AI is not good with that kind of nuance: is it an engineering question or a question about engineering?

If AI can’t easily deal with the subtle difference between doing engineering and talking about engineering, then it’s probably not going to do well with the subtle differences within engineering problems that can have huge effects upon the nature of the optimum outcome. It is also going to have difficulty assessing all the systemic issues present, the best way to apply first principles, or the best way to frame. This is because the AI that seems to reason is, at this time, based on Large Language Models - words - and engineering, as I have noted before, is very visual (it’s often in the “mind’s eye”).

Does this mean engineers are safe?

For now, I can’t see AI understanding the challenges of something like reviewing the landscape to determine (or create) the best approach to building a bridge to cross an expanse of deep water. However, I understand that AI is only getting better. It is therefore only a matter of time until AI can do such things. 

I remain unsure how long that will be.

Until then, it will be the same for engineers as what has been said for writers, medical practitioners and others. The question of whether it will be engineers or AI is the wrong question. The premise is that it will be engineers and AI, so the question then becomes: how will engineers use AI?

And it’s probably going to be pretty good.
Be the engineer you wanted to be
Anecdote time. 
When I was in academia, I would sometimes ask my students who of them wanted to be the type of engineer who understands the fundamentals of theory and first principles, but wants to be more an ideas person who then gets other engineers to make the ideas happen. Everyone would put their hand up. I did this to show my students that the chances of them getting such a job, given the popularity of such a job, is minimal. That means they were all going to have to make the ideas happen as well as coming up with the ideas.
However, AI has probably proved me wrong.
While I think it will be some time until AI can truly do engineering work, I can see it, fairly soon, doing a lot of the grunt work so that we can be the ideas engineer.
How would this work?
Software engineers are showing us how this could play out. They have felt the brunt of AI more than others, because, out of all the engineering disciplines, software engineering is the most language based. And a Large Language Model is ideal for that kind of work.
But while software engineers have been hit the hardest, they have also shown that they are valuable when it comes to the initial idea and providing the right prompt.
That’s what could very well be the case for the rest of us engineers. Consider the following:

• Providing the description for the part you want, and AI then generating the CAD features to produce that part. You can then take a look at the first iteration and describe the changes you want made - then, they would be executed with the speed of AI. If you have been a CAD monkey like me, then you know how much time this could save and the ideas you could generate when you only need to consider the final outcome as opposed to how to generate it.
• Asking AI to generate a circuit board design based on the inputs, outputs, and the available space. Once again, you can review the initial iteration and make suggested changes.
• Using AI to take your initial free body diagram - maybe hand drawn - generate all the force and moment balance equations, solve them for the key parameters you have identified and then optimise for the one parameter you are interested in. You could draw up a number of ideas and have them all analysed near instantly.
• Importing the bill of materials of a product you need to make to an AI system and then asking it to generate the layout of a production line that could make the product. You could then use it to develop details for each work station, estimate the time to complete work, then step back out to see how that affects the overall work flow, iterate, and then do other work stations. With AI to execute your instructions, you could design an optimised production line at an incredible speed.
• ​Have all the parameters set up instantly and to best practice for a CFD simulation. So you need to only focus on analysisng the results, assessing them for validity and then contemplating what changes to implement next.

You can see in the above, that you are automatically like the chief engineer getting other engineers to execute the detailed work for you to review. Imagine also that you can likely talk to the AI system and use peripheral devices to augment your instructions - like when you sketch and use hand gestures to work with colleagues. This is the type of work that many engineers I know would love to do.
The major challenge I see is that we will need to establish how we will train engineers to get to the level where they can be the ones providing the initial instructions. Maybe that is a post for another time.

What will come after this?
There is a chance that one day AI can do our work. I am not one of those people who naively says “AI can never do my job.” But I am not saying I know it can either. So the best thing is to be ready for what could come.

And if AI can one day do all the engineering, including the ingenious stuff, then it has probably also become smarter than us. And If it is that smart, then, like other smart entities (us), it will likely have pets. So work on being adorable to AI so it wants to keep you as a pet and make you happy - not a bad life really.
What are your concerns?
Do you have any thoughts about AI in engineering or any concerns? Share them with me in the comments. And also let me know how you go with Ingeny.
Note
And so you know, while I do get AI to help check my articles, I do write them. Usually on a Sunday night. But I will use AI to often generate my images - if I can’t find a suitable one online - and I sometimes dictate the article content to be cleaned up and written (this one was typed though).