Lead AI Platform Engineer

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. 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. Role - 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. 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. 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