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 this 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.

Key Responsibilities

Leads and mentors engineers across Data Engineering, ML Engineering, and AI Ops, fostering a culture of technical excellence, experimentation, and production-grade AI delivery at scaleDesigns, builds, and operates end-to-end AI Ops platforms supporting machine learning, generative AI, and agentic workflows, from data ingestion and feature engineering through model training, deployment, monitoring, and lifecycle managementHands-on development of AI-enabled systems, including ML pipelines, LLM-based applications, retrieval-augmented generation (RAG), prompt pipelines, agent orchestration, and model inference servicesDefines and implements scalable data and feature pipelines optimized for AI/ML workloads, ensuring high data quality, lineage, reproducibility, and compliance with enterprise governance standardsLeads MLOps and LLMOps practices, including CI/CD for models, automated testing and validation, model versioning, experiment tracking, drift detection, performance monitoring, and rollback strategiesOversees integration of diverse structured and unstructured data sources (batch and streaming) to support analytics, ML, and GenAI use cases across global infrastructure operationsPartners closely with infrastructure, platform, security, and product teams to embed AI capabilities into operational systems, observability platforms, reliability engineering, and automation workflowsConducts architecture and design reviews for AI platforms, data systems, and ML pipelines, ensuring solutions meet scalability, reliability, security, and cost-efficiency requirementsDrives AI Ops automation initiatives, leveraging ML and GenAI to improve incident detection, root cause analysis, capacity forecasting, anomaly detection, and self-healing infrastructureMonitors and optimizes AI and data workflows, ensuring adherence to delivery timelines, sprint commitments, and best practices in DevOps, DataOps, and AI OpsInfluences enterprise AI strategy by evaluating emerging AI/ML technologies, frameworks, and platforms, and guiding their adoption in a regulated, production environment

Education and Knowledge

Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent practical experience; advanced degree preferredStrong knowledge of machine learning fundamentals, including supervised/unsupervised learning, time-series, NLP, and model evaluation techniquesHands-on knowledge of Generative AI and LLM ecosystems, including transformers, embeddings, vector databases, prompt engineering, RAG patterns, and agentic frameworksDeep understanding of data platform and storage technologies, including relational, NoSQL, columnar, graph, and vector storesKnowledge of distributed systems and cloud-native architectures, including containerization, orchestration, and service-based designFamiliarity with model governance, explainability, bias detection, and AI risk management in enterprise environmentsStrong understanding of data formats and APIs (JSON, Parquet, Avro, XML), schema management, and metadata systems

Work Experience

Significant experience in data engineering, ML engineering, or AI platform engineering rolesStrong hands-on programming experience in Python (required); experience with Java, Scala, or similar languages is a plusExperience building and operating ML pipelines and AI platforms using tools such as Airflow, Kubeflow, MLflow, SageMaker, Vertex AI, or equivalentExperience with GenAI frameworks and tooling (e.g., LangChain, LlamaIndex, OpenAI/Vertex APIs, vector databases like Pinecone, FAISS, or similar)Experience designing and scaling large-scale data systems across technologies such as BigQuery, Spanner, Hive, HBase, NoSQL stores, relational databases, and streaming platformsExperience with cloud-based data and AI platforms (AWS, GCP, Azure), including cost optimization and performance tuning for AI workloadsProven experience leading, mentoring, and influencing senior engineers and cross-functional teamsExperience integrating AI solutions into infrastructure, observability, reliability engineering, or operational platforms is strongly preferredExperience with production-grade CI/CD, monitoring, and automation for data and AI systems

Salary Range: $144,250.00 to $256,250.00 annually + bonus + equity (if applicable) + benefits

The above represents the expected salary range for this job requisition. Ultimately, in determining your pay, we’ll consider your location, experience, and other job-related factors.

We 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.

American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. American Express will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable state and local laws, including, but not limited to, the California Fair Chance Act, the Los Angeles County Fair Chance Ordinance for Employers, and the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance. For positions covered by federal and/or state banking regulations, American Express will comply with such regulations as it relates to the consideration of applicants with criminal convictions.

We back our colleagues with the support they need to thrive, professionally and personally. That's why we have Amex Flex, our enterprise working model that provides greater flexibility to colleagues while ensuring we preserve the important aspects of our unique in-person culture. Depending on role and business needs, colleagues will either work onsite, in a hybrid model (combination of in-office and virtual days) or fully virtually.

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Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.