Lead AI Engineer
Mastercard3 days ago
Responsibilities
- Design, build, and operate enterprise AI platforms for machine learning, generative AI, and advanced analytics workloads.
- Engineer scalable capabilities across public and private clouds with strong security, reliability, availability, and performance.
- Automate onboarding, deployment, operations, lifecycle management, and repeatable MLOps workflows.
- Develop infrastructure, tooling, and services for model training, evaluation, deployment, monitoring, and governance.
- Implement observability through telemetry, performance monitoring, logging, alerting, operational analytics, and drift detection.
- Support production AI platforms through troubleshooting, root cause analysis, operational support, and incident response.
- Partner with engineering, infrastructure, security, architecture, governance, and business teams to enable platform adoption and meet enterprise standards.
- Evaluate emerging AI platform technologies, contribute to architecture and strategy, drive engineering best practices, and mentor peers.
Requirements
- Experience designing, building, and operating cloud-native enterprise systems across public and private clouds.
- Experience deploying and managing containerized workloads with Kubernetes or OpenShift.
- Strong software engineering and automation experience using Python.
- Experience with CI/CD pipelines, modern DevOps practices, monitoring, observability, telemetry, logging, and operational analytics.
- Experience supporting production AI, machine learning, data platforms, or large-scale distributed systems, with a strong understanding of MLOps and machine learning lifecycle management.
- Preferred experience includes generative AI and LLM workloads, model evaluation, fine-tuning, guardrails, governance, model observability, drift detection, AI serving, inference platforms, GPU workloads, OpenShift, Kubernetes, Docker, Helm, and Infrastructure as Code.
- Experience with enterprise platform engineering, developer enablement, self-service capabilities, vector databases, retrieval systems, AI gateways, agentic systems, and regulated environments is beneficial.
- Strong troubleshooting, analytical, communication, collaboration, and problem-solving skills.
Benefits
- Opportunity to shape and operate foundational AI platforms used across Mastercard.
- Work across public and private cloud environments on platform scalability, reliability, observability, governance, automation, and customer enablement.
- Collaborative environment involving software engineering, data science, infrastructure, security, architecture, governance, and business stakeholders.
Tech Stack
Categories
About Mastercard
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.