Principal AI Engineer
Mastercard1 day ago
Dublin, IrelandStaff+
Responsibilities
- Advise on technical feasibility, architecture, capability needs, and risks for new business opportunities.
- Partner with product, business, sourcing, and engineering teams to shape hypotheses, solution concepts, business cases, and feasibility assessments.
- Design and evolve agentic and AI-enabled capabilities, including taxonomies, prompt architectures, agent workflows, evaluation frameworks, and supporting intelligence systems.
- Evaluate build, partner, invest, acquire, or discontinue pathways and provide technical recommendations.
- Guide architecture, platform, and capability decisions across incubation, prototyping, market testing, commercialization, and scaling.
- Define target-state architectures, reusable capabilities, architectural patterns, platforms, and assets across opportunities.
- Ensure solutions align with technology standards, security, reliability, operating, governance, and scalability requirements.
- Mentor and influence engineers, architects, and product partners while contributing to engineering standards and architectural decisions.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
- At least 8 years of full-stack engineering experience building distributed, production-grade software systems in an agile production environment.
- Strong backend engineering experience with Java, Kotlin, Python, Scala, or similar technologies.
- Experience leading complex technical features or platforms spanning multiple people, teams, or systems.
- Strong understanding of APIs, services, asynchronous workflows, distributed systems, and enterprise software architecture.
- Deep knowledge of authentication, authorization, secure service-to-service communication, identity, permissions, and policy enforcement.
- Experience operating systems with high reliability, availability, regulatory, security, compliance, and performance requirements.
- Experience building or integrating systems using LLMs, AI services, agent frameworks, workflow engines, orchestration platforms, or related technologies.
- Familiarity with agent orchestration patterns including supervisors, routers, planners, stateful workflows, tool invocation, retries, error handling, and compensating actions.
- High proficiency with Python or Scala, Spark, and SQL.
- Familiarity with AWS EMR, Aurora, Databricks, Snowflake, and cloud technologies for data-intensive applications.
- Experience building and deploying production-level data-driven applications, data processing workflows, pipelines, or machine learning systems at scale.
- Strong technical judgment, analytical and quantitative problem-solving skills, collaboration, communication, creativity, self-direction, and comfort working in ambiguous environments.
- Experience coaching, mentoring, and influencing engineers, peers, and cross-functional partners.
Categories
About Mastercard
Mastercard builds and operates a global payments network used by banks, merchants, fintechs, and governments, offering card processing, real-time payments, tokenization, and fraud/risk services. It generates revenue from transaction processing and assessment/service fees across more than 200 countries and territories. Founded in 1966 and headquartered in Purchase, New York, Mastercard is a public company listed on the NYSE (ticker: MA).