Embedded Data & AI Platform Engineer

Sentior, Ltd.; Veldhoven; Hybrid; EUR 90,000 - 130,000; Full time

Job summary

ASML is seeking an embedded Forward Deployed Platform Engineer to join AI product teams in Veldhoven. You will embed with teams, resolve blockers on the spot and design ingestion, processing, and serving components within platform standards.

You will enable governance through Unity Catalog, trusted datasets and MLflow model registration, while optimizing pipelines for cost, reliability, and lead times in both streaming and batch workloads.

Qualifications

• Minimum 8 years of data platform engineering experience in an enterprise environment.
• Hands-on Databricks (Spark, Delta Lake, Unity Catalog, Workflows) on Azure.
• Experience with production pipelines at scale (streaming and batch).
• Proven cost optimization and observability capabilities.
• Senior Python engineering with IaC (Terraform) and CI/CD in Azure DevOps.
• Basic Kubernetes knowledge; ML/GenAI workloads on a platform.

Responsibilities

• Serve as first technical contact for product teams bringing AI use cases to production.
• Embed with AI application teams and unblock blockers on the spot.
• Design, build and optimize ingestion, processing and serving components.
• Ensure governance via Unity Catalog and trusted datasets; MLflow model lifecycle.
• Improve observability with OpenTelemetry, Azure Log Analytics and ADX/KQL.
• Support ML/GenAI workloads including model serving and vector search.
• Document work and create reusable templates and accelerators.
• Implement IaC with Terraform and CI/CD in Azure DevOps per platform standards.

Skills

Databricks experience

Python

Azure

CI/CD

Terraform

Kubernetes

OpenTelemetry

MLflow

Cost optimization

Tools

Spark

Unity Catalog

Workflows

Azure DevOps

ADX/KQL

Job description

ASML is seeking an embedded Forward Deployed Platform Engineer to join AI product teams in Veldhoven. You will embed with teams, resolve blockers on the spot and design ingestion, processing, and serving components within platform standards.

You will enable governance through Unity Catalog, trusted datasets and MLflow model registration, while optimizing pipelines for cost, reliability, and lead times in both streaming and batch workloads.