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Every Artificial Intelligence system has a bill to pay in computation, energy and latency: those who cut it without sacrificing accuracy and robustness practise one of the most technical and least visible crafts in the whole sector. The AI Algorithm Engineer, between mathematical modelling, performance optimisation and a European market that for ten years has absorbed specialists faster than it can train them.
There is a question that every finance department, sooner or later, puts to AI projects: why does it cost so much to run? The answer, almost always, lives in the least-discussed layer of the chain: the algorithms, their design and their optimisation. This is the territory of the AI Algorithm Engineer, the figure who designs, develops, validates and brings into production AI algorithms, tending to their robustness, computational efficiency, transparency and sustainability.
The core of the craft: from the problem to the computable model
In the US occupational repertoires this label has no slot of its own: the core of the craft can, however, be read clearly in the related occupation of computer research scientists, for which the Occupational Information Network (O*NET) of the US Department of Labor lists the task of “Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by computers”: turning a problem into a computable mathematical model, and that model into code that holds up in the real world.
Industry practice fills in the rest. A posting published by one of the world’s leading makers of computing accelerators asks the algorithm engineer to “understand, analyze, profile, and optimize deep learning training and inference workloads on state-of-the-art hardware and software platforms” (NVIDIA, via AnitaB.org): to profile execution, hunt down bottlenecks, and squeeze performance out of hardware and parallel-computing libraries.
The three optimisations: performance, cost, sustainability
In practice, the value of this profile is measured on three converging fronts. The first is performance: cutting training times and inference latency by choosing the architectures, numerical precisions and implementations suited to the constraint. The second is cost: the same function, poorly implemented, can cost multiples in computing resources; algorithmic optimisation is an economic lever before it is a technical one. The third is sustainability: the energy consumption of AI is by now a line in the budget and in accountability, and reducing it begins with the algorithms, not the press releases.
To these is added the front of reliability: systematic tests of correctness and stability, behaviour at the edges of the domain, reproducibility of results, and traceability of assumptions, versions and parameters. Documentation which, for systems falling within the high-risk categories of Regulation (EU) 2024/1689 (the AI Act), directly feeds the provider’s technical-documentation obligations.
A market that absorbs more than it trains
As for demand, the European figures speak for themselves. The Union’s statistical office records that “from 2015 to 2025, the number of ICT specialists in the EU increased by 59.4%, more than 6 times the increase (9.8%) in total employment” (Eurostat): in ten years the growth of digital specialists ran six times faster than overall employment. And that is not all: “in 2023, 57.5% of EU enterprises that recruited or tried to recruit ICT specialists had difficulties in filling ICT vacancies” (Eurostat). For the more mathematical profiles of the chain, competition with the large international laboratories makes the scarcity even sharper: those who can optimise algorithms find a market on both sides of the Atlantic.
The Italian benchmark
With the UNI 11621-8:2026 standard, this profile too has, in Italy, a perimeter of competences defined according to the methodology of the European e-Competence Framework, the UNI EN 16234-1 standard, with the possibility of having professional competences attested, following assessment by an accredited body, under the UNI CEI EN ISO/IEC 17024 standard, within the framework of Accredia Information Circular DC No. 21/2026. For so specialist a craft, third-party verification is a guarantee for employers and clients alike.
Conclusions
The AI Algorithm Engineer is the silent builder of AI: their work is not seen in the interface, but it decides the performance, cost, consumption and robustness of what users will use. In an economy that measures Artificial Intelligence also in energy bills, it is one of the most strategic competences in the sector.
In the light of the above, one wonders whether the rush to applications will recognise the value of this foundational engineering; every system that stands the test of production, of real data and of time owes it, in large part, to those who tended its algorithms when no one was watching.
