Data Engineering Engineer (AI)

TL;DR

Data Engineering Engineer (AI): Building, scaling, and operationalizing AI-driven diagnostics, observability, and predictive maintenance solutions with an accent on machine learning models, data pipelines, and domain-specific analytics. Focus on owning end-to-end model lifecycles, designing cloud-native data platforms, and translating diagnostic needs into actionable data and AI requirements.

Shanghai, China

Company

hirify.global is a leading provider of semiconductor manufacturing equipment focused on advancing lithography and diagnostics technologies.

What you will do

• Design, develop, deploy, and maintain ML/DL models for predictive maintenance, fault detection, classification, root-cause analysis, and observability.
• Own full model lifecycle including feature engineering, validation, deployment, monitoring, and retraining based on field feedback.
• Build scalable cloud-native data pipelines using Azure, Databricks, Spark, and Kusto for ingesting and transforming machine data.
• Ensure data quality, traceability, and reproducibility while enabling early POC access and production transitions.
• Collaborate with diagnostics experts to improve observability, define data signals, and embed solutions in workflows.
• Define standards, ensure compliance, and communicate impacts like MTTR/MTBF improvements to stakeholders.

Requirements

• Master’s degree in Data Science, Computer Science, Engineering, Applied Mathematics, or related field.
• 5+ years in data science, data engineering, or advanced analytics.
• Strong Python proficiency with ML libraries; scripting in PERL, Bash, PowerShell.
• Proven production ML/DL model development and deployment.
• Strong experience with cloud platforms (Azure preferred), Databricks, Spark, SQL/Kusto, ETL.
• Solid statistics, data analysis, SPC/FDC knowledge; experience with high-frequency data streams.
• Legally authorized to access controlled technology per US Export Administration Regulations prior to starting.

Nice to have

• Experience with diagnostics, manufacturing, equipment data, or industrial systems.
• Familiarity with hirify.global machine data and CS diagnostics workflows.
• Improving observability, fault detection, or predictive maintenance in complex systems.
• Working with business stakeholders and explaining technical results to non-technical audiences.
• Training others and creating technical documentation.