Senior AI Engineer
On-siteDublin, Leinster, Ireland
Job Summary
Develop and contribute to AI and agentic systems across the full lifecycle from design through production deployment. Build and operate ML/AI services, pipelines, and APIs using strong software engineering practices, including model serving, monitoring, evaluation, and retraining. Partner with data scientists to productionize models and experiments efficiently, while participating in technical design reviews to ensure solutions meet standards for performance, reliability, security, and governance. Collaborate with platform, security, and infrastructure teams to ship responsibly at scale. This senior individual contributor role sits at the intersection of software engineering, machine learning engineering, and applied data science within the CNPF Data & AI organization.
Required Qualifications
- Solid experience as a hands-on AI engineer, ML engineer, or software engineer working on production AI systems
- Strong foundations in software engineering, system design, and distributed systems
- Practical experience productionising machine learning models and supporting their operation at scale
- Comfortable working across data engineering, ML engineering, and applied data science tasks
- Familiarity with large-scale data platforms and modern ML/AI tooling
- Good problem-solving skills with the ability to navigate ambiguous requirements
- Collaborative and communicative, able to work effectively across functions and disciplines
- Abide by Mastercard's security policies and practices
- Ensure the confidentiality and integrity of the information being accessed
- Report any suspected information security violation or breach
- Complete all mandatory security trainings in accordance with Mastercard's guidelines
Desired Qualifications
- You have contributed to AI or agentic applications running in real production environments
- Hands-on experience with agent-based or LLM-powered systems beyond simple POCs
- Good instincts for reliability, observability, and failure handling in AI systems
- Ability to move between engineering execution and applied modelling depending on what the problem needs
- Eagerness to grow technically and contribute positively to the engineering culture around you
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