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

Requirements

• Required Qualifications
• 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
• 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
• Working Location : ASML Hwasung Campus
• Inclusion and diversity
• ASML is

Additional Information

Introduction to the job 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. Role and responsibilities 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. Education and experience 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

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