AI Platform Engineer

Building AI infrastructure that's both empowering and compliant. You're not just spinning up cloud resources, you're creating the foundation for how (y) our colleagues will interact with AI.

By ideating and realizing the infrastructure that turns our ambitious AI goals into AI reality. As an AI Platform Engineer, you’ll design and optimize the platforms that enable data scientists and AI users across bol to experiment, iterate, and deploy AI solutions with confidence. When teams can move from idea to production without friction, innovation accelerates. And that directly translates into smarter recommendations, faster answers, and – most importantly – better experiences for 2,900 colleagues, 46,500 partners, and 13.7 million customers.

Building AI infrastructure that’s both empowering and compliant. You’re not just spinning up cloud resources, you’re creating the foundation for how (y) our colleagues will interact with AI. How do you design self‑service tooling that’s flexible enough for experimentation yet robust enough for production? How do you embed compliance and governance without slowing teams down? To maximize your impact, you’ll need to empower us to embrace emerging AI capabilities without jeopardizing enterprise‑grade reliability.

You’ll join the newly formed AI Infra Team, positioned at the intersection of Data Infrastructure, Platform Engineering, and Data Science. Working closely with data engineers, data scientists, and platform colleagues, you’ll integrate modern Google Cloud AI tools – think Vertex AI, Agentspace, and Notebook

LM – into bol’s extensive data landscape. Your ambition: to make AI experimentation frictionless, scalable, and compliant. You’ll contribute to MLOps best practices, automate pipelines, and help define the golden path that guides teams effortlessly from initial prototypes to scalable production solutions. In a team that’s still very much taking shape, you’ll have real and tangible influence over the technical direction and ways of working.

Short version:

• Design and develop golden paths for data scientists and AI practitioners within bol's infrastructure
• Experience with Vertex AI, Agentspace, Notebook
  
  LM, MCP servers
• Build and automate data and model pipelines using Python, GCP (Big Query, Cloud Storage), and CI/CD tooling
• Contribute to MLOps standards: model deployment, monitoring, lifecycle management, and governance
• Collaborate with Data Infra, Platform Engineering, and Security teams to ensure compliant AI workloads
• Improve the developer experience and reliability across the AI/ML ecosystem
• Participate in design reviews, RFCs, and architecture discussions

You combine AI or ML engineering experience with a solid software engineering foundation. You’ve worked hands‑on with cloud platforms (preferably GCP) before, and understand the complexity and pitfalls of building systems that others depend on. Familiarity with Notebook

LM, Agentspace, or similar collaborative AI environments is obviously a plus. You have a deep understanding of MLOps principles: reproducibility, observability, continuous training, and governance, know your way around Python, and you’re comfortable with containerization and Kubernetes. Beyond the technical, you truly care about user impact, enjoy collaborating with cross‑functional teams, and have a knack for explaining technical concepts lucidly.

• Foundation fanatic You find satisfaction in creating platforms that empower others to succeed
• Curious pragmatist You stay current with AI developments but always ask: "How does this solve a real problem?"
• Early‑stage energizer Joining a team that's still forming, with real ownership over its direction and standards? Exciting!

• Model purist You can obsess over perfecting an algorithm for days. Ensuring users get the most out of it, is much less your cup of Darjeeling.
• Your desk is a silo You excel as a soloist and keep a polite distance from data scientists, security experts, and platform engineers
• Certainty preferred If something isn’t set in stone…