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Leads Mastercard’s enterprise platform and data architecture vision, defining architectural patterns, modernization strategies, cloud and integration approaches, and next-generation data capabilities. Oversees architecture governance, security, resilience, scalability, engineering standards, and technology roadmaps. Partners with senior business, product, infrastructure, security, and engineering leaders to deliver strategic capabilities, while building and developing a high-performing architecture organization.

Design and evolve Mastercard’s product experiences and design systems for B2B and B2B2C solutions. Responsibilities include creating reusable components, high-fidelity designs, user flows, journey maps, prototypes, and MVP concepts; collaborating with product, engineering, and research teams; applying human-centered design and design thinking; and improving user engagement, conversion, and design-process efficiency through AI-assisted workflows and rapid prototyping.

Supports business development for Mastercard’s International Institutions vertical by managing partnership pipelines, conducting market and partner analysis, coordinating cross-functional initiatives, tracking workplans and KPIs, and preparing leadership reporting and partner materials. The role supports strategic partnerships with multilateral development banks, development finance institutions, and international organizations across global markets.

Design, build, and deploy transformer-based and generative AI models into production. Develop data pipelines, feature engineering, training workflows, and model serving. Apply MLOps practices, collaborate with product and engineering teams, and support production troubleshooting and performance tuning.

Leads a technical team delivering secure, scalable cloud infrastructure across AWS and Azure. Owns the cloud platform roadmap, reusable infrastructure patterns, governance guardrails, automation, networking, reliability, FinOps, and execution of platform initiatives. Partners with engineering, product, security, architecture, operations, finance, and vendors; reviews designs, manages risks, supports negotiations, and develops teams across geographies.

Design, build, and deploy transformer-based and generative AI models into production. Develop training and inference pipelines, implement model serving and MLOps practices, collaborate with product and data teams, and troubleshoot and optimize model performance in production environments.

Lead lifecycle operations for enterprise load balancer platforms: administer, monitor, patch, back up PKI, troubleshoot complex network/application traffic, automate deployments and monitoring, train operations teams, and collaborate on scalable end-to-end network/application architectures to meet availability and security standards.

Design, develop, and deploy transformer-based and generative AI models into production. Build data pipelines, feature engineering, and training workflows. Implement model serving, inference, monitoring, and MLOps practices. Collaborate with product, data, and engineering teams, participate in code reviews, troubleshoot production model performance, and continuously improve ML engineering practices.

Design, build, and deploy transformer-based and generative AI models into production. Develop training workflows, data pipelines, model serving/inference, and MLOps practices. Collaborate with product, data, and engineering teams to operationalize, monitor, and optimize models.

Design, build, and deploy transformer-based and generative AI models into production. Develop training workflows, data pipelines, feature engineering, and model serving. Implement MLOps best practices (tracking, versioning, testing, monitoring), collaborate with product and engineering teams, perform performance tuning, and support production model troubleshooting.