Principal AI/ML Software Engineer

Description

The company is a global healthcare technology business with about 95,000 employees in more than 150 countries, and its devices reach over 72 million patients a year.

The Interventional Cardiology Therapies unit works on coronary disease and hypertension, pairing devices with digital tools, analytics and AI to help clinicians get better patient outcomes worldwide.

This role blends data science and software engineering to build production machine learning systems, with models that are sound and integrated into scalable software used in real products.

The team values people who think differently and want to make steady progress on hard problems.

Responsibilities

• Lead the technical direction of machine learning and computer vision work for connected healthcare products, from data exploration through deployment, monitoring and ongoing improvement.
• Design, build and evaluate computer vision and machine learning models for complex business and technical problems.
• Explore data, engineer features and run experiments, then validate models with suitable methods to confirm they perform reliably.
• Keep improving models through repeated experiments and development cycles.
• Build end-to-end pipelines covering data ingestion, training, validation and deployment.
• Put models into production as APIs, services or batch pipelines, and track their performance once live.
• Make sure models can be reproduced, versioned and managed across their full lifecycle.
• Lead engineers in writing maintainable, testable Python for ML systems, and promote modular design, automated testing and code review.
• Design scalable services and components that bring ML models into production applications, and guide engineering teams in connecting ML to wider systems.
• Work with cloud infrastructure for ML training and deployment, and build CI/CD processes for pipelines and model releases.
• Automate model retraining and operational monitoring, and keep ML services reliable, scalable and observable.
• Work with data engineers, software engineers and domain experts, turn analysis into practical production systems, and shape architecture for AI-enabled platforms and products.
• Take part in agile/scrum work and cross-functional groups to steer the technical direction of projects.

Requirements

• A bachelor's degree with at least 7 years, a master's degree with at least 5 years, or a PhD with at least 3 years of experience in software engineering, machine learning, artificial intelligence, data science or a related technical field.
• At least 7 years of experience developing, deploying and maintaining machine learning models in production.
• Proven depth in model development, statistical methods, data science techniques and model evaluation.
• Strong Python skills and experience building scalable, maintainable and testable software.
• Experience building ML pipelines, deploying ML to the cloud, running CI/CD and managing model lifecycles.
• Preferred: at least 8 years of experience deploying machine learning models into production.
• Preferred: at least 8 years of experience with computer vision, large datasets and data engineering workflows.
• Preferred: experience designing scalable data pipelines, distributed computing or large-scale ML systems.
• Preferred: experience with Docker and Kubernetes, plus monitoring and observability for ML services.

Conditions

The role is hybrid.

The company is an equal opportunity employer.

The listed statements describe the general nature and level of the work, and are not a complete list of duties or skills.

Benefits

• Competitive base salary plus a flexible benefits package.
• Annual performance bonus, pension contribution, family healthcare coverage and continuous professional development, among other benefits.
• Eligible for the company Incentive Plan (MIP), a short-term incentive.
• Ireland salary range: 83,520.00 EUR to 125,280.00 EUR, with the final figure set by objective, job-related criteria such as experience, skills and qualifications, under the company's compensation framework.