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.
At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.
As part of Team Amex, you'll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.
Education and Knowledge
- 5+ years'' Experience in software development, including hands on experience in machine learning, AI Technologies, and Agentic solutions
- Bachelor''s degree in computer science, Machine Learning, Data Science, and/or comparable experience; advanced degree preferred
- Knowledge of Python and data processing technologies such as Big Data (Hadoop, Spark)
- Knowledge of natural language processing techniques like fuzzy wuzzy and models such as BERT, Transformer models, and their real-world applications
- Advanced knowledge of machine learning techniques and performance optimization
- Strong understanding of AI system architectures including LLM ecosystems, retrieval systems, orchestration frameworks, and evaluation methodologies
Work Experience
- Experience in software development with a focus on machine learning, generative AI, Big Data, and prompt engineering
- Experience in working with large language models (LLMs), especially GPT-3.5/4 or similar AI models
- Experience in languages such as Python, R, Java, or C++
- Experience in deploying AI models and pipelines using machine leanring operations tools
- Experience in machine learning algorithms and experience applying them to real-world problems
- Experience in continuous integration/continuous deployment pipelines and containerized deployments
- Experience in building and maintaining data pipelines and extract, transform, and load processes
- Experience delivering end-to-end AI systems in production, including design and operational ownership
- Experience with distributed systems design, scalable APIs, and cloud-native AI infrastructure, including monitoring and optimization
- Advanced expertise in a technical domain and moderate expertise in a new, different technical domain
- Experience building evaluation harnesses and automated test pipelines for LLM, RAG, or agentic applications using production-representative datasets.
- Experience designing human-in-the-loop and model-based evaluation workflows, including calibration, error analysis, and validation of evaluation quality.
- Experience integrating evaluation results into CI/CD, release readiness, production monitoring, and governance evidence.
Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.
- 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.
- 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.