Senior AI Engineering Data Engineer

Quick Facts

• Role: Senior Data Engineer (AI Engineering)

Description

Build and operate secure Azure data pipelines and data platforms supporting industrial AI scenarios, including analytics, computer vision, edge data processing, and AI-driven insights. Design and integrate data flows from PLC/MES/IoT and enterprise systems into Azure (Fabric/Databricks) while ensuring data quality and compliance with EU AI Act, GDPR, and NIS2. Support MLOps needs through model integration, feature store, and telemetry, with strong testing, monitoring, and governance.

Responsibilities

• Design and implement secure data pipelines from PLC/MES/IoT and ERP/PLM into Azure (Fabric/Databricks)
• Develop scalable ETL/ELT processes (batch and streaming), manage schemas, and orchestrate via CI/CD
• Implement data quality, data lineage, access control, and retention policies
• Support MLOps: integrate models, feature store, and telemetry (e.g., MLflow)
• Define SLA/SLO and ensure data quality via testing and monitoring (freshness, completeness, latency)
• Design data flows for edge computing and low-latency industrial processing
• Coordinate with Manufacturing Excellence, IT, and an AI Governance Board to align with standards
• Collaborate with Data Scientists on features and AI delivery (feature store, data contracts, telemetry, MLOps)
• Produce documentation (runbooks), perform architecture reviews, and manage risks aligned with the AI strategy

Requirements

• 5–10 years of experience in data engineering (production pipelines and AI platforms)
• Practical experience with Azure (Fabric or Databricks), Python, SQL, and PySpark
• CI/CD experience (Git, Azure DevOps), orchestration, and infrastructure as code
• Data governance experience (lineage, role-based access, privacy) and familiarity with EU AI Act, GDPR, and NIS2
• Experience with industrial data (PLC, MES, IoT, time-series) and edge scenarios
• Experience with system integration or industrial automation

Benefits

• Join an international team with knowledge sharing, professional development, and cross-domain collaboration
• Work with modern technologies and influence AI engineering standards
• Contribute to projects with real impact on manufacturing quality, efficiency, and safety
• Broader perspective on global operations and opportunities to grow in architecture, governance, and responsible AI