D&A (Digitalization & Automation) Software Development Engineer (AI) Job at ASML (Hwasung)

Job Description

The D&A Software Development Engineer is responsible for designing, developing, and operating digital, automation, and AI-driven solutions within ASML Operational Excellence. This role owns the end-to-end lifecycle of data, analytics, and AI solutions, with the objective of improving diagnostic efficiency, system availability, and service performance. You will work at the intersection of machine data, diagnostics domain knowledge, and advanced analytics, collaborating closely with CS Diagnostics, Field, D&E, and global platform teams.

Job Responsibility

End-to-End D&A Solution Development
Design and implement full-stack D&A solutions to improve productivity and efficiency for internal stakeholders (office and fab)
Own the complete solution lifecycle, including requirement definition, development, deployment, operation, and continuous improvement
Identify opportunities to reduce or eliminate manual work through automation and software solutions
Provide end-to-end support to internal stakeholders, including structured SDLC activities and urgent ad-hoc requests
AI, Analytics & Model Ownership
Design, develop, deploy, and maintain machine learning and deep learning models for predictive maintenance, fault detection & classification, root-cause analysis, and observability improvement
Perform data exploration, feature engineering, model validation, monitoring, and retraining
Continuously improve model performance based on field feedback, diagnostic outcomes, and new data availability
Data Engineering & Platform Development
Design, develop, and maintain scalable, cloud-native data pipelines for large volumes of structured and unstructured machine data
Work with Azure-based platforms such as Databricks, Spark, SQL, and Kusto to ensure reliable, secure, and high-performance data access
Ensure data quality, traceability, and reproducibility for analytics and AI applications
Support proof-of-concept pipelines and ensure smooth transition to production-grade solutions
Diagnostics Domain Enablement & Collaboration
Translate diagnostics domain needs into data, analytics, and model requirements
Improve observability by identifying data gaps and defining required signals
Collaborate closely with diagnostics experts to ensure solutions are actionable, interpretable, and embedded into diagnostic workflows
Provide training, guidance, and knowledge sharing related to software, data, and analytics solutions
Standards, Governance & Stakeholder Impact
Define and apply standards, policies, and best practices for data, models, and analytics solutions
Ensure solutions are compliant, scalable, maintainable, and secure in line with ASML requirements
Translate technical outcomes into measurable service impact (e.g. MTTR reduction, MTBF improvement, labor-hour savings)
Communicate results, insights, and recommendations to senior stakeholders and leadership

Requirements

Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Applied Mathematics, or a related field
5+ years of experience in data science, data engineering, software development, or advanced analytics roles
Strong ownership of end-to-end AI lifecycle, including problem definition, model development, deployment, and production operations
Hands-on experience developing and deploying machine learning or deep learning models in production environments
Proven experience with machine learning/deep learning frameworks (e.g., TensorFlow, PyTorch), including LLM-based applications
Strong programming skills in Python, with experience in SQL, ETL, and large-scale data processing
Experience with cloud-based data platforms (Azure preferred), including Databricks, Spark, and Kusto
Solid understanding of statistics, data analysis, SPC/FDC concepts, and analytical problem solving
Experience working with large-scale, high-frequency data
Fluent verbal and written communication skills in both Korean and English

Nice to have

Experience with diagnostics, manufacturing, equipment data, or industrial systems
Familiarity with ASML machine data, diagnostics tooling, or CS workflows (e.g., TPMS, FabM, SDT, DDF)
Experience improving observability, fault detection, or predictive maintenance in complex systems
Experience defining data or model standards across teams or platforms
Ability to work effectively with cross-functional and global teams
Experience explaining complex analytical results to non-technical stakeholders

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D&A (Digitalization & Automation) Software Development Engineer (AI)

Job Description

Job Responsibility

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