Aimed at mid-to-senior level AI/ML engineers who can operationalize models within enterprise GPU environments and align with business objectives.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from American Express
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 Implementation / ML Ops Engineer is responsible for implementing, deploying, and supporting AI/ML models within approved GPU-enabled environments. This role packages and operationalizes trained models, supports scalable data and deployment pipelines, integrates models with production systems, and helps maintain reliable monitoring, validation, versioning, and rollback processes. The engineer works within established enterprise patterns, security controls, and AI governance standards while collaborating with product, technology, and business partners to deliver production-ready AI capabilities.
Implements model monitoring and evaluation techniques to improve AI system performance over timeEnsures AI solutions adhere to governance, security, and compliance requirementsProvides support and troubleshooting for AI models and data processing pipelines in productionCollaborates effectively with teams in product and the business to align technology initiatives with business objectivesPerforms standard tasks within established procedures and guidelines; works within established guidelines with some degree of independence for routine tasksDevelops and integrates components of production AI systems, including ML models or LLM-based services, with moderate independenceApplies enterprise standards for observability, safety, and compliance in implemented AI componentsImplements and supports model packaging, validation, versioning, deployment, monitoring, and rollback using approved templates and controls.Contributes to CI/CD, container, Kubernetes, registry, testing, and telemetry configurations for assigned workloads.Troubleshoots common implementation issues and escalates complex design, optimization, or production problems to senior engineers.
Education and Knowledge
2+ years software development, including hands-on experience in machine learning, AI technologies, and Agentic AI solutions.Bachelor's degree in Computer Science, Machine Learning, Data Science, and/or comparable experience; advanced degree preferredKnowledge of Python and data processing technologiesWorking knowledge of machine learning techniques and model evaluation approachesFoundational working knowledge of MLOps or LLMOps, containers, Kubernetes fundamentals, CI/CD, artifact management, testing, and monitoring.Basic understanding of GPU resource needs, runtime dependencies, scheduling constraints, and enterprise security and change controls.
Work Experience
Experience in working with large language models (LLMs), especially GPT-3.5/4 or similar AI modelsExperience in languages such as Python or RExperience in deploying AI models and pipelines using machine learning operations toolsExperience in continuous integration/continuous deployment pipelines and containerized deploymentsExperience developing and deploying machine learning or LLM-enabled applications in production or near-production environmentsPractical experience with distributed systems concepts, APIs, and cloud-native infrastructure supporting AI workloadsModerate expertise in a specific technical domainSome practical experience contributing to software, infrastructure, container, data, or machine learning deployment and automation work.Exposure through project, internship, or practical work to containerized deployment, artifact repositories, automated testing, monitoring, versioning, or rollback.
Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.
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.
Source: American Express careers