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Adyen is seeking a GenAI Platform Engineer to build and scale platform capabilities that enable teams across the company to develop, deploy, and operate LLM-powered applications, agents, and MCP-based integrations. This is a highly technical role at the intersection of platform engineering, distributed systems, and GenAI.
You will develop tooling and infrastructure that improves the developer experience for building, testing, deploying, and operating GenAI systems. Key responsibilities include designing and operating services for model access, tool integration, orchestration, evaluation, observability, and runtime infrastructure; building scalable and reliable infrastructure on Kubernetes; working closely with engineering teams to understand use cases and turn common requirements into reusable platform capabilities; supporting technically complex and high-impact use cases to help teams move from experimentation to production; owning projects end-to-end from architecture through implementation, deployment, and operation; identifying areas for simplification, standardization, and improved reliability; and collaborating in a cross-functional, cross-border environment with teams in Amsterdam, Madrid, San Francisco, and Bangalore.
You will contribute to technical design discussions, code reviews, and engineering standards, launch early, iterate often, and use feedback from internal developers to continuously improve the platform.
Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft. The company values diverse perspectives and creates an environment where people can own their careers.
REQUIREMENTS:
- 4+ years of professional experience in Platform Engineering, DevOps, Data Platform Engineering, MLOps, Backend Engineering, ML Engineering, or similar role
- Hands-on experience with Linux system administration in cloud and on-premises environments
- Strong engineering skills including version control, coding best practices, debugging, testing, and production readiness
- Hands-on experience building or operating LLM-powered applications, agents, or GenAI platforms in production
- Strong platform engineering experience with Kubernetes, Helm, Argo CD, and GitOps practices
- Experience with observability tools such as Prometheus, Grafana, Kibana, or similar
- Proficiency in Python and comfort working with backend services and APIs
- Experience with DevEx and paving the SDLC for users setting up applications on your platform
- Good understanding of LLM application architectures including tool calling, RAG, agent workflows, model gateways, and evaluation
- Ability to diagnose and resolve issues related to reliability, latency, scalability, cost, and deployment of GenAI systems
- Understanding of security, access control, data privacy, and governance for shared AI platform capabilities
- Experimental mindset and comfort launching early, learning from usage, and iterating quickly
- Strong team player with good communication skills and ability to explain technical trade-offs to different audiences
- Comfortable collaborating with cross-functional teams and helping define standards and best practices
DESIRABLE QUALIFICATIONS:
- Experience working with MCPs
- Experience with LLM gateways and model abstraction layers such as LiteLLM, including routing, fallbacks, rate limits, cost controls, and multi-provider setups
- Experience with LLM observability and evaluation tooling such as Langfuse, including tracing, prompt/version tracking, latency and cost monitoring, and production debugging
- Experience with high-performance LLM serving frameworks such as vLLM, including GPU-backed inference, batching, throughput optimization, and serving OpenAI-compatible APIs
