Agentic AI Infrastructure Engineer II - Core Platforms at American Express in New York, NY
- Company: American Express
- Location: New York, NY, United States
- Job type: full time
- Workplace: onsite
- Posted: 2026-10-14
All open roles at American Express
Job description
The Enterprise Technology Services organization partners with every part of the American Express business to power the company’s growth and innovation with trust and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally. Specifically, our team is responsible for the company’s technology engineering, architecture, and infrastructure, providing 24x7 support to ensure an uninterrupted, high-quality experience for customers and colleagues. We also provide product management for core enterprise platforms, and lead technology risk and information security, enterprise data governance and platforms, digital product and design, and enterprise AI platforms on behalf of the company. The AI Infrastructure Engineer is responsible for designing, implementing, and maintaining robust, scalable, and efficient infrastructure solutions that support AI-enabled workloads. This role ensures infrastructure deployments align with technical standards and strategic objectives, proactively resolves operational issues, and provides clear documentation and expert technical support throughout the project lifecycle. Designs the configuration and implementation of infrastructure deployments, ensuring adherence to predefined technical requirements and standards Develops and maintains comprehensive technical design and implementation documentation, ensuring detailed records facilitate seamless infrastructure project execution Supports platform infrastructure availability and performance, proactively identifying and resolving issues or anomalies to maintain optimal operational efficiency Conducts in-depth analysis of current state systems to gather detailed requirements and identify potential improvements in infrastructure design and deployment processes Integrates infrastructure components, ensuring they meet advanced robustness, scalability, and efficiency criteria, and aligning with strategic objectives Conducts test planning and execution activities, validating infrastructure solutions to ensure they meet both business and technical requirements Resolves technical issues encountered during the project lifecycle, providing expert problem-solving and escalating critical issues when necessary Coordinates with project teams to ensure infrastructure components are prepared for production, ensuring readiness and compliance with established standards and protocols Integrates AI-assisted tools and prompt engineering techniques into deployment, monitoring, and infrastructure support workflows to improve efficiency and reduce manual intervention, including validation of AI-generated recommendations, identification of infrastructure anomalies, capacity issues, and deployment risks, support for runbook generation, incident troubleshooting, postmortem summarization, log analysis, infrastructure environments hosting AI-enabled workloads, and operational activities requiring human oversight and approval. Integrates ML-powered AIOps tools into deployment and monitoring workflows to detect anomalies, predict failures, and correlate incidents across distributed systems, including support for custom anomaly detection on metrics, logs, and traces. Applies AI-assisted engineering tools such as GitHub Copilot, Claude Code, Cursor, and similar capabilities to develop, review, and improve infrastructure-as-code artifacts (e.g., Terraform, Ansible, Kubernetes manifests), including security and configuration validation prior to deployment. Designs and operates GPU- and accelerator-enabled compute platforms, including cluster scheduling, workload placement, capacity management, quota and reservation controls, and utilization optimization. Builds reusable platform capabilities for model training, fine-tuning, inference, and agentic workloads using Kubernetes, containers, infrastructure as code, and automated delivery pipelines. Engineers high-performance stora
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