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Tier-1 brand and metro location increase applicant density, but senior, specialized ML platform focus moderates competition.
Enterprise-scale AI platform, governance, and regulated payments experience reduces cross-industry transferability.
Extensive mandatory platform, MLOps, cloud, Kubernetes, and enterprise security experience implies high filtering.
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Design, build, and operate Mastercard's enterprise AI platforms supporting machine learning, generative AI, and advanced analytics workloads across public and private clouds.
Develop and maintain infrastructure and automation tooling for model training, deployment, monitoring, and governance with focus on operational excellence and platform scalability.
Collaborate with engineering, security, governance, and business teams to enable adoption of AI platforms and ensure compliance with enterprise standards and regulatory requirements.
Experience designing, building, and operating cloud-native systems in enterprise environments supporting AI and machine learning workloads.
Proficiency with public and private cloud environments, container orchestration tools like Kubernetes or OpenShift, and automation using Python.
Strong understanding of MLOps principles including CI/CD, monitoring, observability, telemetry, and lifecycle management of machine learning models.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced engineer with strong software development and cloud platform skills focused on scalable, secure, and reliable AI infrastructure in enterprise settings.
Practitioner of MLOps with proven ability to support production AI/ML systems including model governance, performance monitoring, and drift detection.
Able to operate at the intersection of platform engineering, AI operations, and cross-functional collaboration with infrastructure, security, and governance teams.