American Express Technologies is seeking an Engineer with advanced skills in Generative AI and Data Engineering to join our Production Management tooling team. Our mission is to transform how critical platforms are supported by embedding AI-first solutions into event management and system reliability. We are looking for highly motivated, creative problem solvers who are excited to design and implement innovative AI tools that address complex business challenges at scale. Key Responsibilities: Design and develop Generative AI applications, including RAG pipelines, agentic workflows, and Model Context Protocol (MCP) integrations. Collaborate with data owners to build data pipelines, feature engineering processes, and context enrichment strategies that optimize AI-enabled solutions. Prototype and deliver production-ready tools leveraging LLMs, embeddings, vector databases, and orchestration frameworks. Translate business and operational challenges into AI-first solutions with a product mindset, ensuring they deliver measurable improvements in system reliability,incident response, and user experience. Ensure production-ready deployments include appropriate logging, monitoring, and runbooks, maintaining operational excellence alongside innovation. Partner with multiple teams across the enterprise to identify, prioritize, and deliver product-driven GenAI opportunities that improve platforms, tooling, and processes. Minimum Qualifications: Demonstrated passion for Generative AI and its application to real-world business problems. Hands-on experience with LLM frameworks (LangChain, LangGraph, or similar) and RAG architectures. Strong programming skills in Python (preferred) and/or Go, with proven ability to build both prototypes and production applications. Solid understanding of data engineering principles, including ETL pipelines, structured/unstructured data management, and vector databases. Experience with machine learning frameworks such as PyTorch, TensorFlow, and Hugging Face. Proficiency in deploying AI-enabled applications using cloud platforms and containerized environments (Kubernetes, Docker, OpenShift). Preferred Qualifications: Experience developing agentic AI applications and integrating multi-step reasoning into enterprise workflows. Strong grounding in data science techniques, including experimentation, model evaluation, and applied statistical methods. Exposure to observability and monitoring tools such as Splunk, Grafana, Dynatrace, and ELK. Familiarity with APIs, microservices, and web technologies (React, NodeJS, SQL) to deliver end-to-end solutions. 3–5 years of relevant work experience with a Bachelor’s degree in Computer Science, Data Science, or related field.
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