Quality Engineer III

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.

We are seeking a highly skilled and detail-oriented Quality Engineer with strong experience in MLOps platformswithin the FinTech domain. The ideal candidate will play a critical role in ensuring the quality, reliability, scalability, and operational excellence of machine learning platforms that support model development, automated retraining, and deployment across both Batch and Real-Time (RT) processing environments.

Responsibilities

• Design, develop, and execute comprehensive quality engineering strategies for enterprise-scale MLOps platforms.

• Validate and certify end-to-end ML workflows including:

• Model building and training pipelines

• Automated retraining frameworks

• Model versioning and governance

• CI/CD deployment pipelines for ML models

• Batch and Real-Time inference systems

• Collaborate closely with Data Scientists, ML Engineers, Platform Engineering, and Product teams to ensure high-quality model lifecycle management.

• Develop automated testing frameworks for:

• Data validation and integrity checks

• Feature engineering pipelines

• Model performance and drift monitoring

• API and microservices validation

• Real-time streaming and batch processing validation

• Ensure platform compliance with enterprise standards related to security, scalability, resiliency, and regulatory requirements in the financial services domain.

• Support production release validation, rollback testing, observability, and operational readiness for ML deployments.

• Drive quality metrics, defect analysis, and continuous improvement initiatives across the MLOps ecosystem.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related discipline.

• Strong experience in Quality Engineering / QA Automation with exposure to MLOps platforms.

• Proven experience in the FinTech or Financial Services industry.

• Hands-on expertise with ML lifecycle tools and platforms such as:

• MLflow, Kubeflow, SageMaker, Databricks, Vertex AI, or similar

• Jenkins, GitHub Actions, ArgoCD, or CI/CD orchestration tools

• Docker, Kubernetes, OpenShift

• Experience validating both Batch and Real-Time/Streaming architectures using technologies such as Kafka, Spark Streaming, Flink, or similar.

• Strong programming/scripting skills in Python, Java, or similar languages.

• Experience with cloud platforms such as AWS, Azure, or GCP.

• Familiarity with model monitoring, drift detection, explainability, and governance frameworks.

• Strong understanding of test automation, API testing, performance testing, and data quality validation.