Senior Agentic AI Engineer - Core Platforms

Senior Agentic AI Engineer - Core Platforms

+ 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.

The Evaluation Engineer designs, develops, and maintains evaluation frameworks for AI and machine learning solutions, including LLM, RAG, agentic, and multimodal applications. This role translates business, product, risk, and governance requirements into measurable success criteria; builds automated and human-in-the-loop evaluation pipelines; and partners with cross-functional teams to ensure AI systems meet enterprise standards for quality, safety, reliability, compliance, latency, and cost.

** Responsibilities*
* + Mentors junior AI Engineers and cross-functional team of colleagues, fostering a culture of excellence and innovation

+ Integrates AI models into production systems in collaboration with cross-functional teams, ensuring effective integration into existing production systems

+ Maintains strict adherence to model governance, security, and compliance standards

+ Provides guidance and support to junior engineers, fostering professional growth and development within the team

+ Designs and implements AI/machine learning frameworks for data processing, focusing on prompt engineering and generative AI technologies

+ Collaborates with engineers to deliver scalable AI solutions by integrating models that meet both technical and business requirements

+ Translates business requirements into AI-driven technical solutions, ensuring that business needs are converted into actionable models and algorithms

+ Develops reusable Big Data pipelines and AI-based systems, ensuring efficiency and consistency in data processing and model deployment

+ Experiments with and deploys new natural language processing models to classify and analyze business-related text data, conducting research to improve text classification and analysis

+ Creates end-to-end machine learning operations pipelines for scalable model deployment, facilitating seamless deployment, monitoring, and maintenance of AI models

+ Collaborates and co-creates effectively with teams in product and the business to align technology initiatives with business objectives

+ Designs and operates scalable production AI systems, integrating ML models, LLMs, and intelligent services across multiple components

+ Leads development of AI capabilities such as robust inference services, retrieval pipelines (e.g., RAG), orchestration layers, and evaluation frameworks

+ Ensures AI systems adhere to enterprise standards for reliability, safety, explainability, observability, and compliance, mentoring others on best practices

+ Defines evaluation strategies, success criteria, benchmarks, and scorecards for LLM, RAG, agentic, and multimodal AI solutions across quality, safety, reliability, latency, and cost.

+ Builds automated evaluation pipelines using curated datasets, synthetic test generation, human review, model-based grading, and deterministic checks; validates evaluators for consistency and bias.

+ Establishes regression testing, release gates, and continuous evaluation to detect quality degradation, prompt or model drift, unsafe behavior, and changes introduced by models, data, tools, or orchestration.

+ Partners with product, engineering, risk, and governance teams to convert use-case requirements and policy expectations into measurable acceptance criteria and traceable evidence.

+ Performs failure analysis and communicates actionable findings, tradeoffs, and remediation priorities to engineering and business stakeholders.

** Qualifications*
* ** Education and Knowledge*
* + 5+ years' Experience in software development, including hands on experience in…