Forward Deployed Platform Engineer

ASML builds its AI applications on a central Data & AI platform based on Azure and Databricks. The AI Foundation team delivers the platform capabilities on which sector and product teams build their AI solutions, from prototype to production. As Forward Deployed Platform Engineer you do not work from a central platform team at a distance, but embedded in the teams that develop AI applications. You bring platform knowledge into the team, solve on the spot what is holding them back, and feed back to the platform whatever proves reusable.

You are the first technical point of contact for product and sector teams taking AI use cases into production, and you design and build the ingestion, processing and serving components they need, within platform standards. You make governance work instead of block: access through Unity Catalog, trusted datasets, model registration and evaluation through MLflow. The environment is complex and demanding, with high requirements on reliability, and your work directly determines how fast and how safely AI reaches production.

Responsibilities: Act as first technical point of contact for product and sector teams taking AI use cases from prototype to production; Embed with AI application teams and bring platform knowledge into the team, resolving blockers on the spot; Design and build ingestion, processing and serving components together with the teams, within platform standards; Make governance work instead of block: enable access through Unity Catalog and provide trusted datasets; Support model registration and evaluation through MLflow, from experiment to production-ready model; Optimize existing pipelines on cost, stability and lead time, for both streaming and batch workloads; Improve observability of pipelines and AI workloads using tooling such as OpenTelemetry, Azure Log Analytics and ADX/KQL; Support ML and GenAI workloads on the platform, including model serving and vector search; Translate recurring questions from teams into reusable patterns, templates and accelerators for the platform; Implement infrastructure-as-code with Terraform and CI/CD pipelines in Azure DevOps following platform conventions; Explain technical choices to non-platform colleagues and say no with a workable alternative when needed; Document and hand over your work so that teams and the platform can continue without dependency on you.

Requirements: Must-haves: Minimum 8 years of experience in data platform engineering within an enterprise environment; Multiple years of hands-on experience with Databricks (Spark, Delta Lake, Unity Catalog, Workflows) on Azure; Proven experience with production pipelines at scale, both streaming and batch; Demonstrable experience with compute cost optimization; Experience with observability, for example OpenTelemetry, Azure Log Analytics and ADX/KQL; Senior-level Python; Infrastructure-as-code with Terraform and CI/CD in Azure DevOps or comparable tooling; Basic knowledge of Kubernetes; Experience supporting ML and GenAI workloads on a platform: MLflow, model serving and vector search; Proven track record in an embedded or consultative role within business or product teams, and able to explain what that role demands; Strong communication skills in English, able to explain technical choices to non-platform colleagues; Independent way of working, with the ability to say no while offering an alternative.

Nice-to-haves: Experience with the Databricks AI portfolio (Mosaic AI, Agent Bricks, Genie); Experience in high-tech manufacturing or another environment with strict requirements on IP protection and reliability; Experience with SAP-based data sources (HANA Cloud, BW); Knowledge of AI security and governance of agentic applications: access control per agent, auditable logging, drift and hallucination monitoring; Experience with Microsoft Fabric; Dutch language skills are a plus, not a requirement.

Competencies: Strong stakeholder management across platform, product and sector teams; Excellent communication skills, able to translate between technical and non-technical audiences; Analytical mindset with a focus on root causes instead of symptoms; Consultative attitude: listens first, advises with clear alternatives; Experienced in Agile and DevOps ways of working; Strong ownership and accountability for the results you deliver; Proactive in spotting recurring problems and turning them into reusable solutions; Structured and documentation-minded, with attention to handover quality; Pragmatic, balancing speed of delivery with platform standards and governance; Independent and self-driven, comfortable working in an embedded setup.

Tech stack: Azure; Databricks (Spark, Delta Lake, Unity Catalog, Workflows); MLflow; Model serving and vector search; Mosaic AI, Agent Bricks, Genie; Python; Terraform; Azure DevOps (CI/CD); Kubernetes; OpenTelemetry, Azure Log Analytics, ADX/KQL; SAP HANA Cloud, BW; Microsoft Fabric.