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Mastercard

Processes payments across a global network

Lead AI Platform Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • Experience designing, building, and operating cloud-native platforms or large-scale distributed systems in enterprise environments.
  • Strong experience delivering solutions across public and private cloud environments.
  • Experience deploying and managing containerized workloads using Kubernetes or OpenShift.
  • Advanced software engineering and automation experience using Python.
  • Experience leading the technical design and delivery of complex initiatives involving multiple teams or stakeholders.
  • Experience implementing CI/CD pipelines, Infrastructure as Code, automated testing, and modern DevOps practices.
  • Experience designing or supporting production AI, machine learning, data platforms, or distributed systems.
  • Strong understanding of MLOps, model lifecycle management, production inference, and AI system operations.
  • Experience implementing telemetry, monitoring, logging, alerting, and operational analytics.
  • Ability to diagnose complex issues, lead root cause analysis, and implement sustainable corrective actions.
  • Ability to assess architectural options, explain technical tradeoffs, and influence cross-functional decisions.
  • Ability to communicate effectively with technical and non-technical stakeholders.
  • Experience guiding, mentoring, or reviewing the work of other engineers.

Responsibilities

  • Lead the design, development, and operation of enterprise platforms supporting machine learning, generative AI, agentic AI, and advanced analytics workloads.
  • Translate business, engineering, and operational requirements into scalable platform solutions and implementation approaches.
  • Design and maintain advanced AI and machine learning systems that meet enterprise requirements for security, reliability, availability, scalability, and performance.
  • Own technical delivery across public and private cloud environments, identifying dependencies, resolving complex challenges, and influencing cross-functional decisions.
  • Build and optimize services and workflows that simplify onboarding, development, deployment, operations, and lifecycle management for AI solutions.
  • Develop infrastructure, tooling, and services supporting model training, evaluation, fine-tuning, deployment, inference, monitoring, and governance.
  • Lead the implementation of repeatable, scalable, and secure MLOps capabilities and AI development workflows.
  • Build and optimize data ingestion, preprocessing, and feature engineering workflows supporting model training and inference.
  • Design observability capabilities including telemetry, performance monitoring, logging, alerting, operational analytics, model monitoring, and drift detection.
  • Ensure the operational stability and scalability of AI platforms, driving improvements to reliability, resilience, efficiency, security, and customer experience.
  • Lead troubleshooting, root cause analysis, and incident response for complex platform issues and translate lessons learned into sustainable improvements.
  • Influence platform architecture through design discussions, reviews, prototypes, tradeoff analysis, and technical recommendations.
  • Partner with engineering and product teams to understand requirements, guide solution design, enable adoption, and accelerate delivery of AI-powered products.
  • Collaborate with infrastructure, security, architecture, risk, and governance teams to align capabilities with enterprise standards, controls, ethical guidelines, and regulatory requirements.
  • Evaluate emerging AI technologies and industry trends and recommend improvements to Mastercard's AI ecosystem and roadmap.
  • Drive engineering best practices across Infrastructure as Code, CI/CD, automated testing, secure development, documentation, and operational excellence.
  • Guide and mentor engineers through design guidance, technical reviews, problem-solving support, and knowledge sharing.

Desired Qualifications

  • Experience with enterprise generative AI platforms and large language model workloads.
  • Experience with model evaluation, fine-tuning, guardrails, responsible AI controls, and model governance.
  • Experience with model observability, drift detection, telemetry, and operational analytics.
  • Experience with AI serving infrastructure, inference platforms, or AI gateways.
  • Experience supporting GPU-based workloads and accelerated computing environments.
  • Experience with OpenShift, Kubernetes, Docker, Helm, GitOps, and Infrastructure as Code.
  • Experience with enterprise platform engineering, developer enablement, and self-service capabilities.
  • Familiarity with vector databases, retrieval-augmented generation, AI gateways, agentic systems, and model routing.
  • Experience operating in regulated environments requiring strong security, governance, risk management, resiliency, and compliance controls.

About the Company

Mastercard

Processes payments across a global network

Mastercard operates a global payment network connecting financial institutions, merchants, businesses and consumers. It authorizes, clears and settles transactions between participating institutions, while banks issue Mastercard-branded cards and extend credit to their customers. Its services also include fraud detection, identity verification, payment tokenization, data analysis and consulting for organizations managing commerce and financial operations. The company earns network fees linked to payment volumes and processed transactions, along with fees for its services and solutions. Founded by a group of banks in 1966, Mastercard is headquartered in Purchase, within Harrison, New York.

Company size
10,001+
Company stage
IPO
Headquarters
Town of Harrison, New York
Founded
1966

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