Lead AI Platform Engineer (m/f/d)

Decide How AI Gets Built at a Fast-Growing Bike Leasing Company.

Do you want to decide what an AI platform looks like, and care as much about guardrails, evaluation, and cost as about getting the first model deployed?

We are looking for a Lead AI Platform Engineer (m/f/d) to own the AI platform on AWS and Snowflake that data scientists, AI engineers, and business teams build on.

Who are we?

Bike Mobility Services GmbH, a subsidiary of the Dutch company Pon.Bike , is part of a group that owns international brands like Kalkhoff, Cervélo, Urban Arrow, Cannondale, Santa Cruz, Focus, and Royal Dutch Gazelle. Our mission is to get as many employees as possible onto bikes for their commute, through our digital Lease a Bike portal. Data already drives how we steer our portfolio and manage residual values. Now we are building the platform that puts AI into those processes.

Your Misson

You co-own the AI platform with the Head of Data & AI: architecture, roadmap, and the decision what we adopt, build, or buy. You are the hands-on technical lead. Your goal is that ML and AI engineers ship a new model or agent in days, safely and with costs they can see.

Core Responsibilities:

• Architecture & Infrastructure: Set standards for infrastructure, access, and deployment with IT, security, and the ML & AI engineers. Build all AI platform components with Terraform on top of the AWS foundation and GitLab setup of our cloud engineering team.
• LLM & AI Compute: Run AWS Bedrock as our model gateway (access, guardrails, quotas, cost per team), scale SageMaker and GPU workloads, and decide when managed APIs are enough.
• Pipelines & Enablement: Provide orchestration and CI/CD (e.g. Airflow, GitLab), integrated with Snowflake, plus reusable components and self-service deployment, so teams do not rebuild infrastructure for every project.
• Evaluation & Observability: Give teams tracing, evaluation, monitoring, and cost per service for their models and agents, so they can track quality and spend on their own.
• Security & Governance: Implement least-privilege access, audit trails, and technical guardrails for responsible AI. Help non-technical colleagues use approved AI tools safely.
• Team Building: Mentor more junior colleagues and set the engineering standards of the AI team as it grows.

What You Bring

• Must-Haves:
• Platform Experience: 6+ years in platform engineering, DevOps, cloud engineering, or MLOps, with platforms you designed and ran in production.
• AWS, IaC & Security: Strong AWS skills, hands-on Terraform (or AWS CDK), and security built in from the start: access control, secrets, audit logging.
• Pipelines & Data: CI/CD (e.g. GitLab CI), Docker, workflow orchestration (e.g. Airflow), and Snowflake or a comparable cloud data platform.
• ML & LLM Workloads: Experience running ML and LLM workloads in production: managed model access (e.g. AWS Bedrock or comparable), guardrails, evaluation, and cost control.
• Leadership & Ownership: You make architecture decisions you can defend, guide other engineers, use AI coding tools daily, explain trade-offs to non-technical colleagues, and are fluent in English.

Nice-to-Haves:

• Agent Infrastructure: Experience with MCP servers or other infrastructure for AI agents.
• Self-Hosted Models: Experience with Kubernetes and self-hosted model serving (e.g. vLLM).
• Regulation: Working knowledge of GDPR and the EU AI Act.
• Languages: Conversational German or Dutch.

Why Join?

• Real Impact: You decide architecture and roadmap, and what you build in the first year becomes the standard for the AI team.
• Close to the Use Cases: Direct access to the teams behind concrete projects in traditional and agentic AI.
• Bike Leasing & Home Office: We subsidize your bike lease and bike purchase, and we equip your home office so you can work comfortably.
• Learning: You get a learning budget and conference days to grow where you want.
• 100% Remote: You work from anywhere in the EU, and we meet regularly as a team in one of our operating countries, so you get to know the people behind the use cases.

Ready to Build Our AI Platform?

Send us your CV and a few lines about a platform you took into production. If you have any questions please reach out to . The process has two steps: an introduction interview, then a technical conversation. We aim to give you feedback within two weeks of each step.