And what it means for training.
There’s always been overlap between roles and the related training, especially in training for junior tech roles where common base knowledge is important, but these days there’s a definite shift to a range of specific technology that are common across the board.
Consider Platform Engineering. AI Engineering and Full Stack Engineering.
They might have different responsibilities and different entry points into the industry, but increasingly, there’s a set of shared core skills in things that previously might have felt specialised for a specific role.
We’ve been seeing this very clearly while gearing up for upcoming cohorts at Corecom Tech Academy. Content that might once have lived in a “specialist” course is now showing up everywhere, because client requirements demand it for their existing teams, not just clients planning for the future.
Training Maps to Roles
Traditionally, training programmes were built around clear boundaries:
- Developers wrote code
- Testers built manual tests
- Ops teams handled infrastructure
- Security was “someone else’s problem”
- Cloud was a specialism
Teams obviously now work much more collaboratively. This shift has been going on for a while, but increasingly we’re working in a shared set of technology where the boundary between dev, test, ops, cloud etc no longer exists.
Whether someone is:
- building APIs
- maintaining automated tests
- deploying models
- maintaining platforms
- or shipping full-stack features
They’re all operating inside the same complex, distributed, cloud-based systems.
Skills Over Role Labels
When you look at what people actually do day to day, regardless of the role they are doing, there’s an overlap:
- Writing and debugging code (often in more than one language)
- Working comfortably in Linux environments
- Understanding networking well enough to diagnose real problems
- Thinking about security as part of normal development, not an afterthought
- Deploying and maintaining workloads in the cloud
- Integrating with CI/CD pipelines
You can apply these skills to Developers, Testers, Platform Engineers, and many more technical roles. This isn’t nice to have knowledge anymore, it’s baseline competence.
- Full stack engineers don’t just build UIs and APIs; they work in cloud environments, handle secrets, pipelines, and infrastructure decisions.
- Platform engineers don’t just run cloud services; they write code, think critically about applications, and support teams shipping real products.
- AI engineers don’t just train models; they deploy, monitor, scale, secure, and debug them for end users.
- Testers don’t just report failures, they deploy, run and maintain tests in the cloud, integrating with CI / CD pipelines
The Common Link: Cloud and Platform
If there’s one thing pulling all of this together, it’s the cloud. Not because the cloud is new, but because it has become the default execution environment for almost everything: across most roles, and as a result, important ancillary cloud concepts naturally drag in:
- networking
- security
- Linux
- automation
- deployment pipelines
- observability
To name a few.
You can’t teach cloud meaningfully in isolation, and you can’t teach modern tech roles without including cloud technology. That’s why we’re seeing cloud content appear in every course, not just “cloud courses”.
Training Design
All this has an impact on how we design training alongside our clients. If courses are too narrowly scoped around the traditional responsibilities of a specific role:
- they’re missing the wider landscape they’ll be working in
- will take longer to start adding value on real projects
- key skills that allow people to collaborate technically will be missing
- they’ll be less prepared to shift focus or move roles, if required
This doesn’t mean everyone needs to be an expert in everything, or that training needs to be excessively long or complex. It means people need enough breadth to operate effectively, and enough depth to grow once they’re embedded in teams.
When we work with clients to design training, we already know that cloud, platform, CI / CD are going to play a part, but increasingly it’s becoming just as important as the core skills for that specific role.
Final Note on AI
As roles in AI Engineering start to appear more consistently, the platforms, tooling, and processes around AI have also stabilised enough for the role to make sense in day-to-day engineering terms.
A large proportion of AI engineering work is, in practice, normal engineering work. Building and maintaining CI/CD pipelines, working with existing services and APIs, deploying workloads, managing environments, handling security and access, monitoring performance, and debugging failures are all core parts of the role.
We can now prepare people for AI roles in the same way we prepare them for other engineering disciplines – by focusing on how systems are built, deployed, maintained, and improved in cloud first environments, using standard cloud and platform skills.
In that sense, AI hasn’t created an entirely new kind of engineer; it reinforces the value of strong engineering fundamentals, applied to a new and fast-evolving set of tools and use cases.
Conclusion
None of this is about chasing trends or constantly rewriting courses to match the latest job titles. It’s about recognising the environment people are actually working in.
Modern tech roles sit inside shared systems, shared platforms, and shared constraints, and the skills needed to work effectively in that space increasingly overlap.
Training that reflects this doesn’t just prepare people for their first role, it prepares them to adapt as roles evolve, responsibilities shift, and technology changes. That adaptability is what ultimately makes training valuable, both for the people going through it and the teams they join.
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