Fastino

Fastino is an AI company focused on making it easier for teams to build and run useful machine learning models without needing the heavy infrastructure and long training cycles that often come with modern AI. From its positioning, the company appears to concentrate on efficient model development and deployment, aiming to reduce the cost and complexity of getting AI features into real products. For job seekers, that usually translates into work that sits close to core technical constraints, such as performance, reliability, and practical usability, rather than experimentation for its own sake.

The likely users are product and engineering teams that want to add AI capabilities to software they already ship, as well as organisations exploring bespoke models for internal workflows. Fastino’s focus suggests it is designed for people who care about getting models into production and keeping them there, including considerations like speed, inference efficiency, and predictable behaviour. If you have experience working with ML in real-world settings, or you have felt the pain of slow iteration and expensive compute, the problems Fastino is tackling will probably feel familiar.

Within the SaaS ecosystem, Fastino sits in the applied AI infrastructure layer, closer to the tools and platforms that help other companies deliver AI-powered features. Rather than being a vertical SaaS product for one industry, it appears to be building enabling technology that other software businesses can integrate into their own systems. That kind of positioning often means the company needs to balance deep technical work with clear product thinking, because the end goal is to make advanced capabilities accessible to developers and teams who are not AI specialists.

People who tend to thrive in companies like Fastino include machine learning engineers, research engineers, and software engineers who enjoy working at the boundary between research and production. Skills in model training and evaluation, systems engineering, optimisation, and developer tooling are likely to be valuable, as are strong fundamentals in Python and modern ML frameworks. Given the nature of AI infrastructure work, comfort with ambiguity, a bias towards measurable results, and an ability to communicate trade-offs clearly across engineering and product are also likely to be important.

Fastino may appeal if you want to work on foundational technology that can shape how AI is delivered in everyday software, with a strong emphasis on practicality and efficiency. Companies building this kind of platform are often still defining their product surface and engineering standards, which can be attractive if you like ownership, rapid iteration, and the chance to influence technical direction. If you are looking for work where the success metric is not just model quality in isolation, but real-world performance, reliability, and developer experience, Fastino’s mission and problem space are likely to be a good fit.