AI Platform Architect

AI Futures are supporting a search for an AI Platform Architect.

This is a rare opportunity to join a business operating at the centre of Industry 4.0.

Our software powers production systems, connected devices, and IIo T platforms across the world's most demanding industrial environments.

• Your Responsibilities
• Define and own the technical vision for the AI engineering platform - where it needs to go, how it gets there, and what the architecture needs to look like to support the engineering organisation at scale.
• Remain hands-on: design and build platform capabilities, contribute directly to the codebase, review architectures, and set the quality bar through your own work as much as through influence.
• Evolve the existing AI harness and agentic engineering infrastructure — improving reliability, expanding capability, and hardening the platform for production use across diverse engineering teams and environments.
• Build and publish internal SDKs, APIs, and tooling that make AI capabilities accessible to engineers regardless of their AI familiarity.
• Design and implement RAG pipelines over internal codebases, documentation, and engineering knowledge.

Your Profile

• Production-grade AI platform engineering - you've built systems that engineers depend on daily, not demos or prototypes.
• LLM integration at scale: API design, model selection and evaluation, context management, latency and cost optimisation, and the operational realities of running LLMs in production.
• RAG architecture: vector stores, chunking strategies, retrieval evaluation, and the failure modes that only become visible at scale or in a complex codebase.
• Internal developer platforms, SDKs, and APIs - you understand what makes a platform genuinely adoptable versus theoretically elegant.
• Cloud platform depth on at least one of Azure, AWS, or GCP.
• CI/CD, modern engineering practices, and experience integrating AI capabilities into delivery pipelines rather than alongside them.
• Platform observability: logging, metrics, tracing, and evaluation frameworks for AI systems where correctness is harder to measure than uptime.
• Security and governance in a regulated or compliance-aware engineering environment - data handling, access controls, and audit requirements that go beyond standard Saa S defaults.
• Experience working within Manufacturing, Automotive, Industry 4.0.
• Very good German skills and good English skills, both written and spoken

Competitive Salary and total package.

Hybrid Working.

• AI Futures Ltd | Filling the AI skills gap ®
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