Lead AI Platform Engineer
About The Position
The AI Platform Engineering team builds, operates, and evolves Mastercard's enterprise AI platforms, providing scalable, secure, and reliable infrastructure for teams developing and deploying AI-powered solutions. The Lead AI Platform Engineer provides technical leadership in designing, implementing, and operating foundational platforms for enterprise AI and machine learning workloads across public and private cloud environments. This role advances the architecture, engineering, automation, and operational excellence of Mastercard's AI platforms. It leads complex technical design and delivery, improves products and processes, and influences decisions across engineering, infrastructure, security, architecture, and governance teams. The engineer works across platform engineering, cloud infrastructure, MLOps, and AI operations to enable safe development, deployment, observability, and scaling of AI solutions. The role also guides and mentors engineers, reviews technical work, and translates complex requirements into scalable solutions. The position supports capabilities for machine learning, generative AI, agentic AI, and advanced analytics, including model training, evaluation, fine-tuning, deployment, inference, monitoring, and governance. It contributes to platform direction and operational practices while addressing scalability, reliability, security, observability, automation, governance, and customer enablement.
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.
Nice To Haves
- 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.
Responsibilities
- Lead the design, development, and operation of enterprise platforms for 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 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, resolving complex challenges and influencing cross-functional decisions.
- Build and optimize services and workflows that simplify AI solution onboarding, development, deployment, operations, and lifecycle management.
- Develop infrastructure, tooling, and services for model training, evaluation, fine-tuning, deployment, inference, monitoring, and governance.
- Lead implementation of repeatable, scalable, and secure MLOps capabilities and AI development workflows.
- Build and optimize data ingestion, preprocessing, and feature engineering workflows for model training and inference.
- Design observability capabilities, including telemetry, performance monitoring, logging, alerting, operational analytics, model monitoring, and drift detection.
- Ensure platform operational stability and scalability, improving reliability, resilience, efficiency, security, and customer experience.
- Lead troubleshooting, root cause analysis, and incident response for complex platform issues, and implement sustainable improvements based on lessons learned.
- 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 for 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.
- Abide by Mastercard's security policies and practices, protect the confidentiality and integrity of accessed information, report suspected information security violations or breaches, and complete mandatory security training.
Benefits
- Medical, prescription drug, dental, vision, disability, and life insurance.
- Flexible spending account and health savings account.
- Paid leaves, including 16 weeks of new parent leave and up to 20 days of bereavement leave.
- 80 hours of Paid Sick and Safe Time.
- 25 days of vacation time and 5 personal days, prorated based on date of hire.
- 10 annual paid U.S. observed holidays.
- 401(k) with a company match.
- Deferred compensation for eligible roles.
- Fitness reimbursement or on-site fitness facilities.
- Tuition reimbursement eligibility.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
