Senior AI Platform Engineer (#5640)

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 Engineer 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.
Apply an AI-security mindset: prompt injection defenses, PII-in-logs handling, credential handling.
Apply real engineering rigor to the Context Development Lifecycle (CDLC): generate, evaluate, distribute and observe the context that powers AI agents.
Multiply the output and quality of the squad you join, and share patterns/practice beyond your immediate team so adoption compounds across the organisation.
Embed directly into a client squad as a hands-on Individual Contributor (not a coaching/managerial role).

Must-Have Skills:
Kotlin or Python as primary proficiency, with comfort contributing to both.
Experience designing/building/operating shared GenAI infrastructure: LLM routing; vector search and RAG infrastructure; MCP gateway; AI observability and evaluation tooling.
Strong distributed-systems fundamentals: async workflows, idempotency, failure design.
Hands-on production experience with LLM APIs.
AI-security mindset: prompt injection, PII in logs, credential handling.
Agent & harness engineering fluency, with concrete evidence of building/operating this kind of tooling, not just using it.
Comfortable with the client's current daily tools: Claude Code and GitHub Copilot.

Nice-to-Have Skills:
Specific cloud depth (AWS and/or GCP), Grafana, or named LLM API vendors. The RFP describes these capabilities generically rather than naming specific tools for this role — listed here as plausible, not confirmed.