Quality Data & AI Engineer

About the role

We are seeking a highly motivated Data & AI Architect to join our Digital Manufacturing and Engineering Unit. The role sits in the Quality team and focuses on designing, developing and deploying scalable AI, machine‑learning, computer‑vision and data‑analytics solutions that improve manufacturing quality and support data‑driven decision making across global production sites.

Key responsibilities

• Design and implement end‑to‑end AI and data solutions for manufacturing and quality engineering.
• Build machine‑learning and deep‑learning models for defect detection, predictive analytics and process optimisation.
• Develop computer‑vision applications using image processing, CNNs and modern AI techniques.
• Create generative‑AI solutions with large language models, retrieval‑augmented generation and agentic AI workflows.
• Construct scalable data pipelines for collection, transformation and analysis of manufacturing data.
• Produce interactive dashboards and reports using Looker Studio and SQL on BigQuery.
• Develop Python applications and automation tools to streamline engineering and quality workflows.
• Deploy AI applications on Google Cloud Platform (Cloud Run, Cloud Storage, Vertex AI) and integrate enterprise systems, APIs and databases.
• Perform statistical analyses such as capability analysis, Gage R&R and correlation analysis.
• Document technical designs, user guides and deployment procedures.

Required profile

• Master’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering or a related field.
• At least 6 years of experience in manufacturing, automotive, medical devices or industrial automation and in deploying AI solutions to production.
• Strong understanding of software architecture and cloud‑native development.
• Excellent communication and stakeholder‑management skills.

Required skills

• Python programming.
• Google Cloud Platform services (Cloud Run, Cloud Storage, Vertex AI).
• SQL and BigQuery.
• Looker Studio for dashboarding.
• Machine‑learning and deep‑learning model development.
• Computer‑vision techniques (image processing, CNNs).
• Generative AI technologies (LLMs, Retrieval‑Augmented Generation, Agentic AI).
• Statistical analysis methods (Capability Analysis, Gage R&R, Correlation Analysis).
• Design and implementation of scalable data pipelines.