Senior AI Engineer II - Forward Deployed Engineer - Enterprise AI Platforms

American Express•New York, NY

10d

About The Position

The Forward Deployed AI Engineer partners with business units, product teams, and engineering organizations to identify valuable opportunities and turn business challenges into measurable, trusted, secure, and responsible AI solutions. The role builds solutions using Generative AI, agentic systems, and intelligent automation, taking opportunities from discovery and demonstration through proof of value, production scaling, adoption, and transition to long-term ownership. Demonstrations are used to validate value propositions and are not the end goal; work is expected to support real adoption and durable operating practices while meeting enterprise security, reliability, and responsible AI requirements. This is a hands-on engineering role with substantial stakeholder engagement. The engineer influences without direct authority, communicates with people of varied seniority and technical familiarity, works alongside engineers to build and deploy reliable software, and analyzes business processes as systems. The role defines longer-term direction while sequencing practical near-term solutions. Candidates may have backgrounds in mobile, web, backend, distributed systems, or workflow platforms. The role also contributes reusable capabilities and implementation patterns to the Enterprise AI Platform, supports AI-enabled engineering practices, develops evaluation and governance approaches, and helps teams adopt solutions into sustainable operating models. Occasional travel may be required for workshops or key delivery milestones; frequent travel is not expected. Roles may be based near selected American Express offices to support regional business and product partnerships.

Requirements

At least 5 years of experience delivering software, technical solutions, or digital products in an engineering, architecture, or technical consulting capacity.
Demonstrated ability to take an ambiguous business or user problem through discovery, implementation, and measurable validation.
Experience using coding agents in a structured engineering workflow, including specification-driven development, context management, iterative implementation, and reusable knowledge repositories.
Software engineering fluency and ability to contribute directly to applications, integrations, services, or technical prototypes.
Experience in at least one relevant domain, such as mobile, web, backend services, APIs, distributed systems, platform engineering, workflow orchestration, or cloud-native development.
Ability to reason about systems end to end, identify constraints and leverage points, and connect technical decisions to business outcomes.
Strong communication and stakeholder-partnership skills, including explaining technical concepts, constructively challenging existing approaches, and influencing without direct authority.
Ability to define a longer-term direction and sequence incremental solutions that build toward it.
Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience.
Must be eligible to work in the United States for American Express; the company will not pursue visa sponsorship.

Nice To Haves

Experience building or deploying solutions powered by language models, agentic systems, or AI-enabled process flows.
Experience with retrieval-augmented generation, vector databases, tool calling, model context protocols, knowledge retrieval, or workflow technologies such as Temporal, LangGraph, LangChain, or LlamaIndex.
Experience designing human-in-the-loop controls and operational safeguards, with knowledge of AI evaluation, observability, and responsible AI.
Experience supporting technology adoption or operating-model transformation, particularly in financial services or another regulated industry.

Responsibilities

Build trusted relationships and understand business priorities, user needs, operational workflows, and sources of friction.
Challenge existing assumptions, map processes as systems, and identify technical, operational, and organizational changes that could improve them.
Lead discovery sessions to define use cases, value propositions, measurable success criteria, and solution direction.
Assess opportunities for business value, technical feasibility, user impact, risk, and adoption readiness.
Define longer-term visions and identify small, useful interventions that can validate opportunities while preserving a path to broader transformation.
Design, build, and deploy Generative AI, agentic AI, and intelligent automation solutions using approved enterprise platform capabilities.
Develop demonstrations and proofs of value that test assumptions and measure business impact.
Take solutions through prototyping, production scaling, adoption, and transition to long-term ownership.
Build agents and AI-enabled workflows that interact with enterprise APIs, knowledge sources, data platforms, applications, code, and business processes.
Incorporate appropriate human input, approvals, escalation, and fallback mechanisms into solutions.
Work alongside engineering teams to produce software and apply production-ready architecture, testing, deployment, and operational practices.
Use experimentation selectively to reduce uncertainty while balancing speed with security, reliability, maintainability, and governance.
Use coding agents and AI-assisted engineering techniques to accelerate discovery, implementation, testing, documentation, and iteration.
Apply specification-driven development, context management, iterative planning, and structured knowledge repositories.
Evaluate agent-generated code and technical outputs for correctness, security, maintainability, and architectural alignment.
Identify where AI-enabled development practices can improve engineering throughput without compromising operational standards.
Generalize engagement patterns and components for broader use.
Partner with platform and product teams to turn successful prototypes into reusable features, accelerators, reference architectures, and paved-road implementation patterns.
Capture deployment lessons and use them to influence the enterprise AI platform and product roadmap.
Help teams adopt AI-enabled workflows and transition solutions into sustainable operating models.
Develop evaluation approaches that measure solution quality, task completion, business outcomes, user experience, latency, cost, and reliability.
Identify and address failure modes through testing, monitoring, safeguards, and iterative improvement.
Ensure solutions meet American Express requirements for security, privacy, risk management, responsible AI, and regulatory compliance.
Establish appropriate controls for model access, enterprise data, tool use, human oversight, and production operations.
Communicate technical concepts, tradeoffs, risks, and recommendations to audiences with different levels of technical familiarity and seniority.
Influence decisions and drive alignment across business, product, engineering, risk, and platform teams without direct reporting authority.
Maintain stakeholder relationships throughout discovery, delivery, adoption, and transition, and mentor partner teams on AI solution delivery.