Lead AI Platform Engineer
Mastercard1 day ago
Base Salary
$140k - $277k/yr
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.
- Develop infrastructure, tooling, and services for model training, evaluation, fine-tuning, deployment, inference, monitoring, and governance.
- Build scalable MLOps capabilities, AI development workflows, data ingestion and feature-engineering pipelines, and AI platform self-service capabilities.
- Design observability including telemetry, performance monitoring, logging, alerting, model monitoring, drift detection, and operational analytics.
- Own technical delivery across public and private clouds and lead architecture, design reviews, tradeoff analysis, and technical recommendations.
- Lead troubleshooting, root-cause analysis, incident response, and reliability improvements for complex platform issues.
- Collaborate with engineering, product, infrastructure, security, architecture, risk, and governance teams.
- Evaluate emerging AI technologies and contribute to the AI platform roadmap.
- Guide, mentor, and review the work of other engineers.
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 with Kubernetes or OpenShift.
- Advanced software engineering and automation experience using Python.
- Experience leading complex technical initiatives involving multiple teams or stakeholders.
- Experience with CI/CD pipelines, Infrastructure as Code, automated testing, and modern DevOps practices.
- Experience supporting production AI, machine learning, data platforms, or distributed systems, including MLOps, model lifecycle management, production inference, and AI operations.
- Experience implementing telemetry, monitoring, logging, alerting, and operational analytics.
- Ability to diagnose complex issues, lead root-cause analysis, assess architectural tradeoffs, and influence cross-functional decisions.
- Experience guiding, mentoring, or reviewing other engineers.
- Preferred qualifications include enterprise generative AI and large-language-model workloads, model evaluation, fine-tuning, guardrails, responsible AI, model governance, AI serving infrastructure, inference platforms, AI gateways, GPU workloads, Kubernetes, OpenShift, Docker, Helm, enterprise platform engineering, developer enablement, vector databases, retrieval-augmented generation, agentic systems, model routing, and regulated-environment controls.
Benefits
- Full-time employees generally receive medical, prescription drug, dental, vision, disability, and life insurance.
- Benefits include flexible spending and health savings accounts, paid leaves, 16 weeks of new parent leave, bereavement leave, paid sick and safe time, vacation, personal days, and U.S. observed holidays.
- Benefits include a 401(k) with company match, deferred compensation for eligible roles, fitness reimbursement or on-site fitness facilities, and tuition reimbursement.
- The posting lists positions in New York City, Arlington, Atlanta, Austin, Boston, O'Fallon, and Purchase, with the role intended to be filled by January 26, 2027.
Tech Stack
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).