AI and Transformation Engineer

JOB SUMMARY:
Quasar is a global market leader in manufacturing minimally invasive medical devices and components, catheters, and microsensors. We are seeking a hands-on AI Engineer to develop and implement computer vision and predictive AI solutions for manufacturing processes, supporting improvements in product quality, yield, and operational efficiency.
The successful candidate will combine practical AI/ML engineering capability with experience working in manufacturing or industrial environments. This role will translate production challenges into deployable solutions, covering problem definition, data preparation, model development, validation, integration, and ongoing monitoring and maintenance.
Working closely in a cross-functional environment (Engineering, Quality, Production, Continuous Improvement, and IT teams), the AI engineer will help to build in-house manufacturing AI capability and scale successful solutions across Quasar sites.

Essential Responsibilities
1. Manufacturing AI Solution Development
• Work with manufacturing stakeholders to identify opportunities, define problem statements, and establish measurable success criteria.
• Assess data readiness, technical feasibility, and implementation requirements before initiating model development.
• Develop and deploy AI solutions that address manufacturing quality and process challenges.
• Select appropriate approaches, including conventional analytics, statistical methods, machine learning, and deep learning, based on the problem and available data.

2. Computer Vision and AI Inspection
• Develop image classification, object detection, segmentation, and visual anomaly detection models for inspection applications.
• Collaborate with engineering teams on image acquisition requirements, including camera setup, lighting, image quality, and inspection coverage.
• Establish image collection, annotation, and dataset curation processes with Quality and Engineering teams.
• Evaluate models against agreed inspection criteria and build MLOps practice for model performance monitoring and maintenance.

3. Predictive AI and Process Analytics
• Develop predictive AI models using machine parameters, process measurements, inspection results, and test data.
• Implement process drift detection, multivariate anomaly detection, and prediction of downstream quality outcomes.
• Apply feature engineering and appropriate validation methods to time-series and tabular manufacturing data.
• Work with process engineers to interpret model outputs and define practical responses to alerts.

4. Manufacturing Data and Traceability
• Define data collection requirements and collaborate with IT, engineering, and automation teams on machine-data extraction.
• Build and maintain data pipelines for AI development and deployment.
• Establish reliable linkage between machine parameters, images, process steps, and inspection or test outcomes.
• Address missing data, inconsistent timestamps, limited traceability, and insufficient defect examples.


Education and Skill Requirements
• Bachelor's degree in Engineering, Computer Science, Artificial Intelligence, Data Science, or a related discipline.
• 3-5 years of relevant hands-on experience, including implementation of AI/ML solutions in manufacturing or industrial environments.
• Demonstrated experience taking an AI solution beyond academic research or a standalone proof of concept into an operational pilot or production deployment.
• Practical computer vision experience and working knowledge of predictive modeling for process or quality data.
• Strong programming capability in Python and SQL.
• Experience with ML/deep-learning frameworks, image-processing libraries, and data preparation pipelines.
• Experience with industrial cameras, machine connectivity, IoT, or automation-system integration.
• Experience with edge inference, model deployment, and MLOps.
• Understanding of manufacturing processes, inspection requirements, and production-floor implementation constraints.
• Ability to communicate effectively in spoken and written English with global teams.
• Strong problem-solving, stakeholder collaboration, and independent execution skills.
• Exposure to regulated industries (e.g. medical devices, healthcare, pharmaceuticals) is a plus.