EU remote
Senior AI Platform Engineer
About this role
N-iX is a global software development company founded in 2002, connecting over 2,400+ tech professionals across 40+ countries. We deliver innovative technology solutions in cloud computing, data analytics, AI, embedded software, IoT , and more to global industry leaders and Fortune 500 companies. Join us to create technology that drives real change for businesses and people across the world. Our client is a fast-growing European fintech company in the business spend management space — corporate cards and related financial products — serving SME and mid-sized business customers across the EU and UK, in a regulated environment with GDPR compliance obligations.
Engineering is organized into cross-functional squads, with platform/enabling teams providing shared tooling horizontally. The client already runs an AI-native engineering practice: Claude Code and GitHub Copilot are used daily, supported by a growing library of shared, versioned Skills embedded in key repositories, enabling an end-to-end flow from ticket to implementation, testing and PR in several codebases. AI tool/connector rollout follows an approved-list and pilot process.
The client is AWS-first overall; for data platform and analytics workloads it also runs GCP, with BigQuery as the primary data warehouse. Day-to-day coordination is Slack-first, with Linear for ticket tracking, GitHub for code/PR review, and Notion as the knowledge base. The client is also building out its AI platform and agentic capabilities: it recently launched an MCP surface in closed beta, giving external AI assistants a structured way to connect and perform real workflows behind guardrails, and treats agent reliability as a system property (tool contracts, approval/consent gates for write actions, evaluation harnesses).
We're looking for a Senior AI Platform Enginee r to design, build and operate the shared GenAI infrastructure that product teams rely on. Key Responsibilities: Design, build and operate shared GenAI infrastructure: LLM routing, vector search and RAG infrastructure, MCP gateway, and AI observability/evaluation tooling. Apply strong distributed-systems fundamentals: async workflows, idempotency, failure design. Work hands-on with LLM APIs in production.