Remote CTO: Production-Grade AI Platform

most teams never get access to. The job isn't to chase hype. It's to make systems reliable, scalable, and actually useful to the people depending on them. /p p You'll move between layers comfortably. One moment you're deep in AWS, thinking about how to stabilise and monitor pipelines across multiple customers. The next, you're shaping how models (both traditional ML and LLMs) fit into a product that needs to be fast, interpretable, and trusted. Then you're back in the code, making sure it all holds together. /p p This is not about building the most complex system. It's about building the right one. /p p There's a strong bias toward pragmatism here. If a deterministic workflow beats a clever model, you choose the workflow. If an agent adds risk without clear value, you push back. If something can't be explained simply, it's not ready. You'll work closely with product and commercial teams, translating complexity into clarity and helping shape what actually gets built. /p p From

a leadership perspective, this isn't consensus-driven. You're expected to challenge, to question, and to take ownership. The team needs someone who raises the bar, mentors others, and drives decisions forward, especially when things are unclear or uncomfortable. You don't wait for permission. You move things. /p p Technically, you're still very hands-on. Strong software engineering fundamentals are a given. The current environment spans AWS, TypeScript (React / React Native), and data infrastructure (Postgres, MongoDB, pipelines). That said, strong Python backgrounds - especially from ML-heavy environments - are highly relevant. What matters is that you've built real systems, not just models in isolation. /p p Most importantly, you've done this before: taken models, pipelines, or data-heavy systems and turned them into production-grade platforms that scale across customers, use cases, and environments. You've seen what breaks. You've fixed it. And you've made it better the second time. /p p strong Key things we're looking for; /strong /p p Deep, hands-on engineering capability across backend, data, and cloud (AWS) /p p Experience combining ML and LLMs into real, working systems /p p Strong track record of productionising and scaling AI/data platforms /p p Ability to simplify messy systems and make architecture decisions that last /p p Product mindset: you care about outcomes, not just technical elegance /p p Clear communicator who can explain complex ideas simply /p p Proven leadership: mentoring, setting standards, and pushing back when needed /p p strong Things you should know; /strong /p p This is a startup environment: speed, ambiguity, and ownership come with the territory /p p The focus is on scaling and stabilising what exists, not chasing blue-sky ideas /p p You'll be expected to lead from the front, not from a distance, in a fully remote context /p p There is real impact here: what you build directly affects how customers perform in the real world /p p strong FAQ's /strong /p p Is this hands-on? Very. You'll be writing code and shaping systems daily. /p p Is this more ML or engineering? Both but with a strong bias toward production engineering. /p p Do I need TypeScript experience? Helpful, but not essential if your Python/ML background is strong. /p p Is this a leadership role? Yes, but through ownership and action, not hierarchy. /p p Visa sponsorship: Not currently available /p p If you've read this far and this feels like your kind of problem, email me directly at strong ##### /strong with the subject strong Built to Scale /strong and tell me about a system you took from messy to production-ready. /p #J-18808-Ljbffr

p You're the kind of person who doesn't sit on the sidelines drawing diagrams while someone else does the hard work. You build. You break things. You fix them properly. And when something works, you're already thinking about how it survives scale, customers, and reality. /p p Right now, there's a platform already delivering value in a high-performance environment where marginal gains matter, and decisions aren't theoretical - they show up in outcomes that people care about deeply. /p p It's not a blank slate. It's live, in use, and trusted. But under the surface, it's messy in the way fast-moving, ambitious systems tend to be. Pipelines overlap. Models exist, but not all of them belong in production. Architecture decisions need to be made with the next 10x in mind, not the last sprint. /p p This is where you come in. /p p You'll take ownership of an AI-driven product built on a uniquely rich, real-world dataset - one that captures performance, behaviour, and context at a level