Design and build highly scalable, resilient, and low-latency data platforms supporting mission-critical payment workloads.
Develop distributed data processing and real-time streaming pipelines capable of handling high-volume transactional data.
Build AI-ready data architectures and pipelines that enable machine learning, Generative AI, and real-time intelligence use cases.
Explore and implement emerging AI capabilities, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, vector search, and AI agents, where appropriate.
Design and develop microservices, APIs, and event-driven architectures using modern engineering practices.
Drive adoption of cloud-native technologies, containerization, orchestration, CI/CD, automation, and observability.
Establish engineering practices for performance, scalability, resiliency, security, data quality, and operational excellence.
Apply Responsible AI and data governance principles, including privacy, security, explainability, model evaluation, and appropriate controls.
Evaluate emerging technologies across AI, data, and distributed computing and translate them into practical solutions for payment platforms.
Provide technical leadership, mentor engineers, conduct design reviews, and contribute to engineering standards and best practices.
Partner with product, architecture, data science, and engineering teams to turn complex business opportunities into scalable technology solutions.
Minimum Qualifications
Experience designing, implementing, and operating large-scale distributed data platforms and systems.
Strong experience with NoSQL technologies such as Cassandra, Elasticsearch, Couchbase, or Redis.
Experience with large-scale distributed data processing technologies such as Apache Spark.
Strong programming experience with Python, Java, Scala, or similar languages.
Experience designing data pipelines, data models, APIs, and event-driven architectures.
Strong understanding of distributed system concepts, including scalability, reliability, resiliency, security, and performance.
Demonstrated ability to solve complex engineering problems and deliver production-grade solutions.
Preferred Qualifications
Experience building AI/ML-ready data platforms supporting machine learning, Generative AI, or intelligent application use cases.
Familiarity with Generative AI, LLMs, RAG, embeddings, vector databases/search, and AI agent architectures.
Experience integrating AI/ML models and services into production environments.
Familiarity with MLOps/LLMOps, including deployment, evaluation, monitoring, observability, governance, and lifecycle management.
Experience with distributed messaging and streaming platforms such as Apache Kafka.
Experience with real-time distributed processing using technologies such as Spark, Kafka, Cassandra, and Elasticsearch.
Experience developing microservices and cloud-native applications.
Experience with Docker, Kubernetes, OpenShift, or similar container platforms.
Experience with CI/CD, DevOps, infrastructure automation, and production observability.
Understanding of Responsible AI, data privacy, security, governance, and model risk considerations.
Experience architecting large-scale systems with an emphasis on availability, scalability, performance, resiliency, and cost efficiency.
Ability to stay current with emerging AI and data engineering technologies and identify opportunities to apply them to real-world business problems.
Strong communication, collaboration, and technical leadership skills, with the ability to mentor engineers and influence technical direction.
Depending on factors such as business unit requirements, the nature of the position, cost and applicable laws, American Express may provide visa sponsorship for certain positions.
