Founding Platform Engineer — AI & Data Infrastructure

We are partnering with an well-funded, early-stage technology company building a sophisticated AI-native platform from the ground up.

We are looking for a senior systems-oriented engineer to help build the internal technical foundation that every product team will operate on top of — spanning data infrastructure, model serving, experimentation and backtesting, configurable internal workbenches, and agentic AI workflows .

This is not a conventional customer-facing product engineering role. The challenge is to build the systems underneath the products: a highly configurable internal platform that brings together complex data, models, experimentation and human decision-making.

Working closely with the CTO, you will help shape the architecture and build the platform from zero.

What You’ll Build

• Architect the central data backbone , including ingestion, transformation and governance across complex structured and unstructured data sources.
• Build infrastructure for deploying, serving, evaluating and iterating on ML models in production.
• Develop experimentation and backtesting systems that allow teams to test hypotheses and strategies against historical and proprietary data before deployment.
• Build highly configurable internal workbenches that bring together data, models, research and decision workflows.
• Develop agentic AI workflows for research, analysis and validation, integrating LLMs and other models into reliable production systems.
• Build the observability, auditability and governance required for data- and model-intensive systems.
• Work directly with the CTO and senior leadership to turn ambiguous business problems into technical architecture and production systems.

What We’re Looking For

• Significant software engineering experience building complex, production-grade systems, ideally at Staff, Principal or Founding Engineer level .
• Demonstrable experience designing and building a highly configurable internal platform, workbench or operational system end-to-end . This is a core requirement.
• Strong backend and systems architecture fundamentals across distributed systems, APIs, data models, workflows, reliability and scalability.
• Deep experience with data-intensive systems , including ingestion, transformation, orchestration and multiple heterogeneous data sources.
• Experience building infrastructure around ML or quantitative models , including model serving, deployment, evaluation or monitoring.
• Experience building experimentation, backtesting, simulation or evaluation infrastructure where users can test hypotheses against data before production deployment.
• Proven experience building 0→1 systems and making foundational architectural decisions rather than exclusively working within mature infrastructure.
• Ability to take loosely defined requirements and translate them into clear technical architecture and an executable build plan.
• Strong engineering judgement and comfort prioritising functionality, configurability and reliability over visual polish for internal tooling.

Experience with agentic AI, quantitative systems, financial technology or other highly data-intensive environments is valuable, but direct domain experience is not required.

The Profile

• You are a systems builder who gets excited about the architecture underneath a product.
• You can move comfortably across data infrastructure, backend systems, models, experimentation and internal tooling , and understand how those layers need to work together rather than specialising in only one of them.
• You enjoy taking complex problems with no existing playbook and turning them into systems that are robust, configurable and built to evolve.
• You move quickly, challenge assumptions when needed, and want meaningful influence over the technical direction of a company while the foundational architecture is still being defined.

Why Join

This is an opportunity to join a well-capitalised company at the beginning of its technical build , working directly with an experienced CTO and founding team.

The organisation will remain deliberately lean and highly technical, giving early engineers genuine influence over the architecture, engineering culture and systems on which the company is built.

If you want to build at the intersection of **AI, data infrastructure, model systems and experimentation — from zero — this is an unusually a…