Задачи: As AI Data Engineer (f/m/d) in GE HealthCare's Advanced Imaging Solutions business, you will develop reusable tools, operate shared data infrastructure, and evaluate platforms for ultrasound teams worldwide. At GE HealthCare, our machines, our software, our solutions, our services, and our people make a genuine difference to medical professionals and patients all over the world. That’s because we never lose sight of what healthcare really needs—the human touch. This position is hired in Zipf, Austia in hybrid work (min. 3 days per week in office). AI Data Engineer (f/m/d) As AI Data Engineer in GE HealthCare's Advanced Imaging Solutions business, you will develop reusable tools, operate shared data infrastructure, and evaluate platforms for ultrasound teams worldwide. GE HealthCare develops medical technology, pharmaceutical diagnostics, and digital solutions that help clinicians make informed decisions and improve patient outcomes. With more than a century of healthcare experience, we pursue a world where healthcare has no limits and embrace respect, transparency, integrity, and diversity. Working with local teams and infrastructure owners, you will turn requirements into tested solutions and help teams adopt and operate common tools. Work with AI technical leaders to translate cross-team requirements, reference architectures, and priorities into working solutions and operational procedures. Design and run reproducible experiments, proofs of concept, and benchmarks on third-party and internal data management and AI development platforms using representative ultrasound team workflows. Assess functional fit, interoperability, performance, scalability, usability, integration effort, and operating cost against agreed requirements. Document methods, results, limitations,



and recommendations to inform platform selection. Collaborate with individual teams to extend internally developed tools for use across the ultrasound organization. Refactor team-specific assumptions into configurable modules and standardize interfaces to increase reuse. Develop and maintain shared libraries, services, and connectors, with automated tests, versioned packages, and deployment workflows that make adoption and maintenance easier for other teams. Build and operate data ingestion, transformation, and validation pipelines supporting data curation, annotation, dataset preparation, and AI development. Carry out day-to-day operation of agreed shared tools and environments, including configuration, updates, job monitoring, troubleshooting, and performance tuning, in coordination with platform owners. Support teams with platform onboarding, tool integration, and data or workflow migrations, and provide practical feedback on gaps in shared infrastructure. Implement data quality checks, logging, and traceability, and automate approved data access, de-identification, and retention workflows with guidance from the responsible specialists. Investigate pipeline and platform failures, reproduce issues, and work with internal platform teams or external vendors to resolve root causes and prevent recurrence. Create user guides, integration examples, and operational runbooks; provide training and technical support so teams can adopt, operate,



and contribute to shared tools. Требования: Expected Qualification: Master's degree in computer science, data engineering, software engineering, information systems, AI, or a related technical field. Hands-on experience developing and operating data pipelines, shared software tools, or AI development infrastructure. Practical knowledge of cloud environments, containers, Linux, and data workflow orchestration. Strong programming skills in Python and SQL, with experience designing APIs, integrating services, and working with structured and unstructured data. Experience with Git, automated testing, code review, continuous integration and delivery, and maintainable software packaging. Ability to design controlled technical experiments, benchmark platforms, and communicate results and trade-offs clearly. Understanding of AI data workflows, including curation, annotation, dataset versioning, and training and validation data preparation. Awareness of security, privacy, and traceability requirements when handling healthcare data. Fluent English and the ability to collaborate with globally distributed engineering, data science, and infrastructure teams. Desired Qualification: PhD in computer science, data engineering, software engineering, information systems, AI, or a related technical field Experience with medical imaging or ultrasound data and healthcare data formats. Experience adapting team-developed software into reusable components adopted by multiple teams. Familiarity with data catalogs, annotation tools, experiment tracking, model registries, and AI development platforms. Experience with infrastructure as code, observability, and improving the reliability or cost of data processing. Personal Attribut

📌 AI Data Engineer (Requena)
🏢 gehc
📍 Requena