Senior Ai Platform Engineer (#5640)

N-iXis a global software development company founded in **, 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 clientis 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).a Senior AI Platform Engineerto 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.#J-***-Ljbffr

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.

is a global software development company founded in **, 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). a Senior AI Platform Engineer to design, build and operate the shared GenAI infrastructure that product teams rely on.