Agentic AI Infrastructure Engineer II - Core Platforms

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.

Qualifications

• Experience designing infrastructure for AI agents, including agent runtimes, orchestration frameworks, tool integrations, asynchronous execution, and stateful workflows. Examples include LangGraph, Semantic Kernel, and comparable frameworks.
• Experience applying Agentic AI tools to support infrastructure engineering, platform support, or cloud operations work, including reviewing AI-generated operational insights.
• Experience validating AI-generated operational outputs and incorporating AI-enabled practices into infrastructure workflows, including AI-assisted operational recommendations requiring human review and approval.
• Experience with Kubernetes-based AI platforms, containers, workload schedulers, and GPU or accelerator orchestration in cloud or hybrid environments.
• Experience operating model-serving or inference platforms and optimizing throughput, latency, availability, and cost for production
• AI workloads.
• Experience with distributed training or inference infrastructure, high-performance networking and storage, accelerator telemetry, and capacity planning.
• Experience implementing security, reliability engineering, observability, and FinOps practices for multi-tenant AI infrastructure.
• Experience in different programming languages, DevOps tools such as Ansible, Puppet, Terraform, Python, Shell scripting and PowerShell, and their applications in infrastructure.
• Experience in infrastructure components such as servers, storage, networking, and virtualization technologies.
• Experience in network protocols and their job in infrastructure management.
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• Experience in networking fundamentals.
• Experience in databases such as DB2, SQL Server, Oracle
• , MySQL.
• Experience in tools used in planning, analyzing, crafting, building, testing, configuring, and maintaining applications.
• Experience in cloud platforms (e.g., AWS, Azure, Google Cloud).
• Experience in monitoring technologies such as Prometheus, ElasticSearch and Grafana.
• Education and Knowledge
• 3 to 5 Years o f experience in infrastructure engineering, cloud engineering, DevOps, or a related technical field, including at least 2 years of hands-on experience supporting AI-enabled infrastructure, AI automation, or agentic AI technologies.
• Bachelor's degree in Computer Science, Information Technology, Engineering, and/or comparable experience; advanced degree preferred.
• Knowledge of application infrastructure and standards.
• Knowledge of storage management (e.g., SAN, NAS) design and support
• Knowledge of middleware technologies.
• Knowledge of software defined networking, automation, and orchestration and their applications.
• Knowledge of platform delivery methodology (Agile, Scrum, Agile XP, Best in Class Development, Waterfall, etc.).
• Knowledge of scripting and automation skills (e.g., PowerShell, Bash, Python).
• Knowledge of DevOps practices and tools (e.g., continuous integration/continuous deployment pipelines, Docker, Kubernetes).
• Knowledge of cloud technologies.
• Knowledge of Windows and Linux administration.
• Working knowledge of AI-assisted infrastructure automation, observability, and deployment support capabilities, and infrastructure services commonly used to support AI-enabled workloads.
• Employment eligibility to work with American Express in the U.S. is required as the company will not pursue visa sponsorship for these positions.

Responsibilities

• 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 storage, networking, and data-movement patterns required for distributed AI workloads, with measurable service-level objectives for availability, latency, throughput, and recovery.
• Implements end-to-end observability and FinOps controls for AI infrastructure, including workload telemetry, accelerator utilization, cost attribution, anomaly detection, performance tuning, and capacity forecasting.
• Partners with AI engineers, security, risk, and platform teams to establish secure multi-tenant isolation, identity and access controls, secrets management, software supply-chain controls, and operational readiness.