Senior AI Engineering

Senior Staff AI Engineering

New York, NY, United States

Sunrise, FL, United States

Phoenix, AZ, United States

(Hybrid)

** Job Description*
* 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

Senior Staff AI Engineer leads enterprise-scale AI architecture and implementation across infrastructure and agentic AI systems. The role sets technical direction, ensures scalable, reliable, secure, and responsible AI platforms, mentors senior technical talent, and drives continuous improvement in AI engineering practices and platform capabilities.

** Responsibilities*
* + Serves as a senior technical authority for enterprise-scale AI systems with significant strategic and long-term business impact

+ Defines architecture and engineering patterns for agentic AI, retrieval/grounding, model integration, inference platforms, observability, evaluation, and safety

+ Leads resolution of complex and ambiguous technical challenges, balancing performance, cost, scalability, security, safety, and regulatory constraints

+ Shapes long-term AI engineering strategy across platforms, tools, and system design approaches

+ Guides multiple cross-team AI initiatives to ensure coherent architecture, integration, and alignment across domains

+ Leads technical and architectural reviews, setting standards for engineering excellence and consistent implementation

+ Partners with Product, Data Engineering, Cloud Infrastructure, Architecture, Risk, and Cybersecurity leaders to align AI solutions with enterprise priorities and governance requirements

+ Evaluates emerging AI technologies and translates them into scalable, enterprise-ready capabilities

+ Mentors Staff and Senior Engineers, raising organizational technical capability and developing future technical leaders

** Qualifications*
* ** Education and Knowledge*
* + MS/PhD in Artificial Intelligence, Machine Learning, Computer Science, or related discipline preferred

+ Deep knowledge of machine learning and deep learning systems, including model architectures, training, evaluation, and optimization

+ Advanced knowledge of Generative AI and LLM ecosystems, including embeddings, fine-tuning, prompt design, retrieval-augmented generation, and inference at scale

+ Strong understanding of AI infrastructure, including GPU platforms, accelerated compute, workload scheduling, capacity optimization, model serving, and inference performance

+ Advanced understanding of agentic AI design, including planning, reasoning, tool use, memory, multi-agent coordination, and autonomy controls

+ Strong foundation in distributed systems, cloud-native architecture, Kubernetes, APIs, microservices, event-driven design, observability, and platform reliability

+ Knowledge of Dev Ops and CI/CD platforms such as Harness, including automated deployment, environment promotion, governance, and operational controls

+ Knowledge of enterprise AI governance, including model risk management, explainability, bias detection, safety, and regulatory compliance

** Work Experience*
* + 12+ years of experience in AI/ML engineering, AI infrastructure, platform engineering, data engineering, or related fields, with a track record of delivering complex production systems at scale

+ Proven experience architecting end-to-end AI/ML platforms across data pipelines, training, deployment, serving, monitoring, and inference optimization

+ Experience designing and operating GPU-based AI infrastructure, including accelerated compute platforms, workload scheduling, utilization optimization, capacity management, and performance tuning

+

Experience with cloud-native and Kubernetes-based platforms supporting training, batch workloads, inference, orchestration, observability, reliability, and cost efficiency

+ Hands-on experience with Python and modern AI/ML frameworks such as PyTorch, Tensor Flow, Scikit-learn, Hugging Face, and related tooling

+ Deep experience with agentic AI systems, including planning, tool use, memory, evaluation, retrieval, embeddings, vector databases, and agent frameworks

+ Experience leading complex cross-functional technical initiatives and influencing architecture and engineering direction across teams without direct authority

+ Demonstrated ability to mentor and develop engineers at all levels, including…