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.
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.
About UsAt 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.
About the TeamWe back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally:
- Competitive base salaries
- Bonus incentives
- 6% Company Match on retirement savings plan
- Free financial coaching and financial well-being support
- Comprehensive medical, dental, vision, life insurance, and disability benefits
- Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
- 20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy
- Free access to global on-site wellness centers staffed with nurses and doctors (depending on location)
- Free and confidential counseling support through our Healthy Minds program
- Career development and training opportunities
For a full list of Team Amex benefits, visit our Colleague Benefits Site.
Skills Required
- 5+ years of software development experience with hands-on machine learning, AI technologies, and agentic solutions
- Bachelor's degree in computer science, machine learning, data science, or comparable experience
- Knowledge of Python and big data processing technologies such as Hadoop and Spark
- Knowledge of natural language processing, fuzzy matching, BERT, and Transformer models
- Advanced knowledge of machine learning techniques and performance optimization
- Strong understanding of LLM ecosystems, retrieval systems, orchestration frameworks, and evaluation methodologies
- Experience with software development focused on machine learning, generative AI, big data, and prompt engineering
- Experience working with large language models, especially GPT-3.5/4 or similar models
- Experience with Python, R, Java, or C++
- Experience deploying AI models and pipelines using MLOps tools
- Experience applying machine learning algorithms to real-world problems
- Experience with CI/CD pipelines and containerized deployments
- Experience building and maintaining data pipelines and ETL processes
- Experience delivering end-to-end AI systems in production with design and operational ownership
- Experience with distributed systems, scalable APIs, cloud-native AI infrastructure, monitoring, and optimization
- Advanced expertise in one technical domain and moderate expertise in a different technical domain
- Experience building evaluation harnesses and automated test pipelines for LLM, RAG, or agentic applications
- Experience designing human-in-the-loop and model-based evaluation workflows, including calibration, error analysis, and validation
- Experience integrating evaluation results into CI/CD, release readiness, production monitoring, and governance evidence
- Advanced degree
American Express Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about American Express and has not been reviewed or approved by American Express.
-
Fair & Transparent Compensation — Pay is considered generally solid for many, with frequent mentions of good compensation alongside strong benefits. Overall sentiment points to many employees feeling adequately paid even if it is not viewed as top-of-market.
American Express Insights
What We Do
At American Express, we know that with the right backing, people and businesses have the power to progress in incredible ways. Whether we’re supporting our customers’ financial confidence to move ahead, taking commerce to new heights, or encouraging people to explore the world, our colleagues are constantly striving to uphold our powerful backing promise to our customers and each other every day. These beliefs have been our North Star for 170 years as our business transformed – from helping evacuate travelers during World Wars, to ensuring the safety of our customers’ funds during the Great Depression in the U.S., to creating the Shop Small® movement to help small businesses recover from the Financial Crisis, to providing aid to communities impacted by many natural disasters and so much more. For generations, the key to our success has been the determination and resilience of our American Express colleagues. Now, as a globally integrated payments company, we work together to provide customers with access to products, insights and world-class experiences that enrich lives and build business success. Join us and let’s lead the way together.
