When companies pause hiring, software teams are often the first to feel it. At the same time, new roles are appearing around internal tooling and AI engineering.
Teams are getting smaller and roles broader. Engineers are increasingly expected to understand the product, the customer or the design as well as the code. Full stack roles are often written as product engineering roles, where an engineer owns a feature through to release, and the same title now stretches across front end, data and AI work. Full stack rarely means an even split, and where a role is clearly back end or front end, clients increasingly want an out-and-out specialist.
Domain experience counts for more than it did. Regulated businesses usually hire people who have already worked in regulated environments, and many clients now want someone from exactly the same industry, where previously most strong engineers would have been considered. Smaller companies can be more flexible on sector, but they look for a different kind of fit, usually startup or scale-up experience, or work on an AI product.
AI is changing the interview as well as the work. Processes are longer than they were, partly because companies skipped stages a few years ago and have since put them back. Off-the-shelf coding tests are giving way to challenges that show how someone approaches a problem, works with stakeholders and uses AI. Rules on AI use in interviews still vary from one company to the next.
Engineers are open to opportunities but rarely search actively, and the strongest move through networks rather than adverts. Competition is heaviest for the few who combine broad experience with real AI work. Office attendance is where searches most often stall: one or two days is rarely a problem, four or five narrows the pool quickly, and some candidates ask for more pay to cover the commute. Pay expectations above company budget are the other common blocker.
