Principal AI Engineer

About the role

Mastercard is looking for a Principal AI Engineer to lead the design and delivery of an enterprise‑scale AI platform. You will combine deep software engineering expertise with cloud architecture to enable secure, reliable AI‑powered applications across the organization.

Key responsibilities

• Design scalable architectures for AI applications, distributed services, APIs, event‑driven workflows and data‑intensive workloads.
• Build production‑grade backend services, orchestration frameworks and reusable platform components.
• Define platform patterns, reference architectures and implementation standards to accelerate AI adoption.
• Translate ambiguous business requirements into secure, scalable technical solutions that meet governance and security reviews.
• Drive architectural decisions across service boundaries, data flows, performance, extensibility, reliability and security.
• Design and evolve cloud‑native foundations including Kubernetes, containers, networking, deployment automation and infrastructure as code.
• Establish standards for observability, SLOs, deployment/rollback, capacity planning, resiliency and disaster recovery.
• Produce architecture diagrams, flowcharts and design documentation; lead architecture, security and governance reviews.
• Contribute hands‑on through development, code reviews, design reviews and technical coaching.
• Evaluate emerging AI, cloud and platform technologies and guide practical adoption.

Required profile

• Strong engineering experience designing, delivering and operating large‑scale production systems.
• Proven ownership of complex platform, infrastructure or enterprise software solutions from design through production.
• Experience leading technical initiatives across multiple teams and influencing architecture decisions at scale.
• Hands‑on experience with Python and modern backend technologies.
• Experience designing APIs, distributed systems, event‑driven architectures and cloud‑native applications.
• Deep understanding of software design principles, testing strategies, CI/CD and continuous delivery practices.

Required skills

• Python
• Kubernetes
• Containers
• APIs
• Distributed systems
• Event‑driven architecture
• Cloud‑native application development
• Infrastructure as code
• CI/CD pipelines