Senior AI Engineer II - Forward Deployment, Enterprise AI Platforms

• - 5 or more 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 the 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 the ability to explain technical concepts, challenge existing approaches constructively, and influence 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.

• - Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.
   

Preferred Qualifications

• - 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.

Success Measures

Success in this role will be measured by:

• - AI opportunities that progress from discovery to proof of value and, where appropriate, production adoption.

• - Measurable business outcomes tied to clearly defined KPIs.

• - Ability to identify and reframe valuable opportunities while connecting near-term delivery to longer-term transformation.

• - Solutions that improve the broader workflow and are adopted into sustainable operating models.

• - Strong stakeholder relationships, technical quality, and effective management of security, risk, and reliability.

• - Reusable capabilities and implementation patterns incorporated into the enterprise AI platform.

Working Model and Travel

Occasional travel may be required for workshops or key delivery milestones, but frequent travel is not expected. Roles may be based near selected American Express offices to support close partnerships with regional business and product teams.

Opportunity discovery and transformation

• - Build trusted relationships and develop a deep understanding of business priorities, user needs, operational workflows, and sources of friction.

• - Challenge existing assumptions, map processes as systems, and identify the technical, operational, and organizational changes most likely to improve them.

• - Lead discovery sessions that translate business needs into clear use cases, value propositions, measurable success criteria, and solution direction.

• - Assess opportunities based on business value, technical feasibility, user impact, risk, and readiness for adoption.

• - Define a longer-term vision and identify the smallest useful intervention that can validate the opportunity while preserving a path to broader transformation.

Solution design and delivery

• - Design, build, and deploy Generative AI, agentic AI, and intelligent automation solutions using approved enterprise platform capabilities.

• - Develop working 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, approval, escalation, and fallback mechanisms.

• - Work directly alongside engineering teams to produce software and apply the architecture, testing, deployment, and operational practices required for production use.

• - Use experimentation selectively to reduce uncertainty while balancing speed with security, reliability, maintainability, and governance.

AI-enabled engineering practices

• - 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 to preserve context and improve personal productivity.

• - 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.

Platform contribution and scale

• - Generalize patterns and components developed through individual engagements 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 lessons from deployments 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.

Evaluation, governance, and responsible AI

• - 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.

Stakeholder leadership

• - 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 relying on direct reporting authority.

• - Maintain strong stakeholder relationships throughout discovery, delivery, adoption, and transition, and mentor partner teams on AI solution delivery.