Agentic AI Infrastructure Engineer II - Core Platforms

• 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.

• 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.

Education and Knowledge

• 3 to 5 Years of 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.