Senior AI Engineer 1

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.

Bachelor's degree in Computer Science, Information Systems, or equivalent experience, with 8+ years of professional software engineering experience, including meaningful production experience with LLMs or applied ML systems.
- Coding & Algorithms: Strong programming skills in Python and proficiency in at least one other object-oriented language (e.g., Java, C/C++, or Go), with deep command of data structures, algorithms, and complexity trade-offs, and the ability to produce clean, correct code under pressure.
- Machine Learning Domain: Strong grasp of ML and GenAI concepts - model architectures (Transformers, CNNs), loss functions, bias-variance trade-offs, precision/recall, and model optimization such as quantization (INT8/FP8).
- ML System Design: Ability to tackle abstract, large-scale design problems (e.g., RAG pipelines, real-time inference, recommendation systems) with emphasis on high availability, low latency, and scale.
- Behavioral / Leadership: Proven ability to handle ambiguity, navigate cross-functional conflict, collaborate effectively, and demonstrate technical leadership and mentorship.
- A track record of designing, shipping, and operating AI-powered or agentic systems that real users depend on in production.
- Strong engineering fundamentals across backend systems, APIs, distributed microservices, data pipelines, and cloud infrastructure.
- Solid grasp of end-to-end system design, including databases (SQL/NoSQL), distributed messaging (e.g., Kafka, Redis), caching, and the trade-offs of low-latency, high-throughput systems.
- Experience building and deploying cloud-native applications; familiarity with Docker, Kubernetes, or similar platforms.
- Hands-on DevOps/MLOps experience - CI/CD, containerization, monitoring, and productionizing and maintaining solutions.
- Fluency with AI-assisted and agentic development workflows and tooling.
- Strong sense of ownership, sound technical judgment, and comfort operating with autonomy and ambiguity.
- A strong product mindset and customer orientation.

Good to Have

- Experience building AI or agentic systems in fintech or other regulated industries.
- Experience with vector databases, advanced retrieval, and large-scale data pipelines.
- Deep experience with agentic frameworks and orchestration (e.g., LangChain, LangGraph, AutoGen).

- Serve as a technical leader on an agile team, turning complex, ambiguous problems into production-grade AI and agentic systems that meet security, compliance, and regulatory requirements.
- Design, build, and ship LLM-powered and agentic features end to end, from shaping solutions with product and design, to writing production code, to owning performance and reliability after launch.
- Contribute to both high-level architecture and detailed design of complex subsystems, with emphasis on high availability, scalability, and low latency.
- Build and integrate AI capabilities (LLMs, agents, RAG, orchestration, tool-use) into distributed, cloud-native services and data pipelines.
- Lead code reviews, drive consistent development practices, and champion automated testing and evaluation frameworks for AI systems (including prompt, model, and agent evaluation).
- Debug and troubleshoot software and model-related issues, identify defects, and drive remediation.
- Mentor engineers, influence the team's technical strategy, and help prioritize initiatives.