Sr AI Engineer II - Enterprise AI Platforms

American Express•Sunrise, FL

5d•$123,000 - $215,250

About The Position

American Express is building an enterprise-grade Agentic AI Platform that enables teams to safely build, deploy, operate, discover, govern, and continuously improve AI agents at scale. The Senior AI Engineer will design and build foundational platform capabilities at the intersection of AI systems, distributed platforms, cloud infrastructure, developer tooling, and enterprise governance. This hands-on role focuses on building platform infrastructure that enables teams and agents to operate securely and reliably across the enterprise, rather than only developing individual AI applications. The engineer will contribute to agent runtime and execution, a unified enterprise control plane, governance and security, agent discovery, observability, evaluation and continuous learning, developer experience, and AI-specific CI/CD and platform engineering. The role includes building scalable, cloud-native systems and abstractions across internally managed and third-party execution environments, AI providers, models, tools, and runtimes. The successful candidate will help developers move from an agent idea to a governed production deployment through a consistent self-service platform. Platform capabilities should make secure and reliable practices the easiest path, enabling agents to be built once and operated across AI ecosystems with governance, identity, observability, evaluation, deployment, and operational capabilities built in.

Requirements

Strong software engineering experience building production systems using languages such as Python, Java, Go, or TypeScript.
Strong understanding of Generative AI, LLMs, agent architectures, tool/function calling, retrieval, context management, and agent orchestration.
Experience building production applications or platforms using major model providers or AI platforms.
Experience with Kubernetes, containers, microservices, distributed systems, and cloud-native architecture.
Experience designing production APIs and event-driven or asynchronous systems.
Strong understanding of modern cloud infrastructure and infrastructure-as-code practices.
Experience with CI/CD, automated testing, production observability, and software delivery practices.
Strong understanding of security fundamentals including identity, authentication, authorization, secrets, and least-privilege access.
Ability to navigate ambiguous technical problems and turn emerging technologies into reliable production systems.
Strong communication skills and ability to collaborate across engineering, architecture, product, security, and governance organizations.
Bachelor’s degree required.

Nice To Haves

Experience with agent frameworks and orchestration technologies such as LangGraph, Semantic Kernel, Google ADK, OpenAI Agents SDK, or similar.
Experience with Model Context Protocol (MCP) and emerging agent interoperability protocols such as A2A.
Experience with multi-agent systems and agent-to-agent communication patterns.
Experience with Kubernetes operators, controllers, service meshes, or sophisticated Kubernetes platform engineering.
Experience with agent/model gateways and intelligent model routing.
Experience with vector databases, retrieval systems, embeddings, and enterprise knowledge architectures.
Experience with LLM and agent evaluation frameworks, LLM-as-judge techniques, human feedback systems, experimentation, and quality measurement.
Experience with AI observability, distributed tracing, OpenTelemetry, Langfuse, and production monitoring.
Experience with Evals.
Experience with policy engines and policy-as-code.
Experience with AI security, prompt injection defenses, tool security, data-loss prevention, and AI-specific threat modeling.
Experience with platform engineering, internal developer platforms, developer portals, and enterprise service catalogs.
Experience with large-scale distributed systems operating in highly regulated environments.

Responsibilities

Design and implement highly scalable components of the enterprise Agentic AI Platform.
Own complex technical problems from architecture through production implementation and operation.
Build distributed services and APIs supporting agent execution and lifecycle management.
Develop integrations with major AI/model ecosystems and managed agent platforms.
Establish common abstractions across models, agents, tools, runtimes, and providers.
Design systems for agent identity, permissions, policy enforcement, and governance.
Build observability, evaluation, and experimentation capabilities for production AI systems.
Develop SDKs, tooling, and automation that improve developer productivity.
Design reliable CI/CD and release processes for AI agents and related artifacts.
Establish patterns for resilience, scalability, performance, security, and cost efficiency.
Evaluate emerging agent frameworks, protocols, models, and infrastructure technologies and determine their fit within the enterprise platform.
Partner with product, architecture, security, risk, infrastructure, and application engineering teams.
Build scalable agent runtime infrastructure using Kubernetes and cloud-native technologies, including orchestration, tool execution, state and context management, memory, asynchronous workloads, event-driven execution, and multi-agent workflows.
Develop a unified control plane and APIs for agent registration, configuration, deployment, versioning, lifecycle management, policy enforcement, and runtime management across execution environments and AI providers.
Build platform controls and mechanisms for governing agent identity, authentication and authorization, tool permissions, data boundaries, auditability, models, prompts, MCP servers, knowledge sources, agent interactions, and external integrations.
Build registries and catalogs with metadata, ownership, versioning, dependency, certification, and discovery mechanisms for agents and other reusable platform capabilities.
Build end-to-end agent telemetry and capabilities for explaining and debugging complex workflows across models, runtimes, tools, and external systems.
Develop evaluation frameworks and automated pipelines using offline evaluations, production signals, human feedback, regression testing, and experimentation.
Build SDKs, APIs, CLIs, templates, local development environments, and self-service workflows that incorporate enterprise standards.
Build AI-specific CI/CD capabilities for evaluation, policy validation, security checks, artifact and version management, deployment, promotion, rollback, and release controls; design GitOps and infrastructure-as-code patterns for agent workloads.
Partner with security, risk, privacy, and governance teams to translate enterprise requirements into scalable technical controls.