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Every Cloud Has An (Intelligent) Lining: AI & clOUD ENGINEERING
I’m Elspeth, a Cloud Engineer and CFG Ambassador. I currently work at BlakYaks, a cloud native consulting, engineering and managed services business in London.
Cloud engineering is often underrepresented in discussions about software engineering and
AI, despite providing the infrastructure that modern software and AI systems rely on. It is a newer area than traditional software engineering. The commercial launch of most prominent cloud services took place over the 2000s and 2010s, with modern cloud disciplines forming in the latter decade.
It is also less well understood outside the tech industry.‘The cloud’ has become a corporate buzzword, thrown into job listings and slide decks with wild abandon. Media reactions to some global cloud infrastructure outages of the last decade, like the Amazon Web Services outage in 2025 and the Microsoft Azure outage in 2023, suggest the area is widely confounding. Yet the outages themselves illustrate how this area of tech underpins global commerce, communication,transportation and finance.
Here I would like to explain what cloud engineering is, some of the key roles of cloud engineers, and how AI can transform cloud engineering. I will also explore how women in cloud roles can benefit from AI.
What Does A Cloud Engineering Role Look Like?
Building The Basics: Infrastructure Management
A cloud engineer builds and maintains cloud infrastructure. Often when discussing cloud
infrastructure people point to the major cloud providers of Microsoft Azure, Amazon Web Services, Google Cloud Platform, and Oracle Cloud. These companies provide services for computing, networking, storage, security and identity. Your role as a cloud engineer includes responsibilities like creating, deploying, testing, and maintaining this infrastructure.
Keeping Systems Healthy: Cloud Observability
Cloud observability is a key part of an engineer’s job: collecting and analysing data from infrastructure and applications. A virtual machine may not have patched correctly, or an OS disk on Azure may be nearing the limits of its disk space; monitoring is essential to catch these issues before they can have a wider impact. For some cloud engineers call-outs in the middle of the night are not uncommon, if there is a critical issue requiring attention and alerts have been fired.
Keeping Costs Low: FinOps and Cost Optimisation
Cost optimisation is an often overlooked part of cloud engineering. Cloud infrastructure, particularly when it comes to large global companies, can cost hundreds of millions a year. It is an essential service, but can become very expensive. Engineers will look to cost optimisation, or the FinOps framework, to combat this. Efficiency is the key: infrastructure should fit the needs and size of a business, not waste money on underutilised resources.
Opening Doors For Women In Cloud Engineering
Women are particularly underrepresented compared to wider tech fields, with female representation in cloud engineering at just 14%, against 25-30% in wider tech. As with wider software engineering, women hold far less senior roles in cloud. However, demand for cloud engineers remains high; over 60% of organisations report internal skills shortages related to cloud technology and emerging tech, and the global cloud computing market is booming, expected to surpass $1 trillion by 2028.
To help women utilise opportunities in cloud engineering there is no one way forward. Early exposure to cloud engineering careers, clear routes into the field through certifications, and flexible career paths such as different working arrangements can address some of the issues. AI is another tool that can be hugely beneficial, as we will see.
How AI Helps Women Win In Cloud Engineering
LEVEL UP YOUR TECH SKILLS WITH AI
One of the key ways AI can help is by accelerating skill development and making cloud education more accessible. As a cloud engineer you need a broad understanding of many disciplines that were once separate roles in software engineering. You also need to keep abreast of the latest updates and new releases; AI can help you digest new concepts and summarise news. This can be as simple as asking an AI chatbot to break down a concept or update you are struggling with, and providing a well-structured prompt that works with your own learning style.
This is something you can use throughout your career as a cloud engineer, as you build on your knowledge and understand how to design and operate complex systems. Female engineers often experience imposter syndrome, and AI offers a way to practice and review as many times as needed, thereby building confidence.
USE AI AS A CODING CO-PILOT
AI is also a useful tool to help write and understand code, for example infrastructure as code and automation scripts. Previously, if you encountered an unfamiliar error message, you might have to trawl through various forums to find the correct information. AI can point you to the answer much quicker, and reduce the time it takes to correct the wider issue.
USE AI AS A CAREER COACH
AI’s benefits extend to interviewing as well; studies have shown that female engineers often feel and project less confidence in job interviews compared to men. An AI chatbot can simulate technical and non-technical interviews using a voice-mode feature, helping you anticipate likely future questions and practice your responses. There are many AI CV builders that can provide templates for specific job roles, and suggest content for cover letters.
Lastly, when trying to break into cloud engineering or progress to a more senior role, it is useful to be able to evaluate your current skills and look at what you should focus on to develop – an AI chatbot can do this for you. This way you can prioritise the knowledge that will have the biggest impact on your career progression.
Work Smarter: AI's Impact On Cloud Engineering Workflows
Now let’s look at the impact AI can have on cloud engineering itself. It has the ability to assist some of the most common frustrations for cloud engineers.
NO MORE LATE-NIGHT PANICS: FIX CLOUD OUTAGES
Imagine you have a callout in the middle of the night, and have to creep over to your laptop to deal with a critical production issue or an alert. The process can be stressful and tiring, particularly if the system that generate the alerts is creating ‘alert noise’. This is when there are excessive or duplicate notifications, that usually do not require any immediate human action. It may include redundant alerts, or false positives that are reporting an issue which does not exist. This situation causes wasted time for engineers, as they must sift through and review countless alerts instead of finding and solving real system errors.
Even before the alert is generated AI can help to design better structure and settings for the alerts, to help the engineer viewing a fired alert to see what is wrong and what is affected. AI can learn from previous incidents to determine what is a critical issue and what is just a brief event, thereby reducing the number of unnecessary callouts. In cases of issues that do require callouts, it can be trained to suggest solutions – subject to human review and internal policies – which will help resolve the issue faster.
STOP SURPRISE CLOUD BILLS WITH AI
Unexpected costs can be a big problem in cloud engineering. Resources accidentally left running after projects finish can incur costs. AI could detect idle resources and recommend shutting them down or deleting them. Engineers may work through billing reports and dashboards for hours before determining what actions to take to reduce waste, but AI could automate this process. By analysing costs across a cloud service, AI could suggest key areas a business should focus on and steps to take to reduce costs. It could also try to predict future trends to anticipate expenditure.
AUTOMATE ADMIN-HEAVY TASKS
Finally, a big opportunity in cloud engineering is through automation. Automation already features heavily in software development methods like CI/CD, or Continuous Integration and Continuous Delivery, and AI presents an opportunity to increase automation and allow engineers to focus on more complex tasks. AI could generate Terraform or Bicep templates to create infrastructure, and engineers could review this and save themselves from writing out hundreds of lines of code. Some cloud platforms include self-healing infrastructure, where systems can detect, diagnose and fix failures or performance issues. With AI these systems can become even more advanced and accurate.
What's Next For AI and Cloud Engineering?
AI is a valuable tool for changing cloud engineering and has potential to vastly change the industry in the future. Cloud services like Azure and AWS have many AI offerings themselves, and we will likely see this grow as more of the industry adopts it.
With the small percentage of female engineers working in the cloud industry it is important to increase accessibility in cloud, and AI is one method to do this. As a seasoned cloud engineer, or a woman looking to break into cloud engineering, it can help you navigate challenges in your learning, work, and career development.
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