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With the right backing, people and businesses have the power to progress in incredible ways. When you join Team Amex, you become part of a global and diverse community of colleagues with an unwavering commitment to back our customers, communities and each other. Here, you’ll learn and grow as we help you create a career journey that’s unique and meaningful to you with benefits, programs, and flexibility that support you personally and professionally.

At American Express, you’ll be recognized for your contributions, leadership, and impact—every colleague has the opportunity to share in the company’s success. Together, we’ll win as a team, striving to uphold ourcompany valuesand powerful backing promise to provide the world’s best customer experience every day. And we’ll do it with the utmost integrity, and in an environment where everyone is seen, heard and feels like they belong.

Join Team Amex and let's lead the way together.

As part of our diverse tech team, you can architect, code and ship software that makes us an essential part of our customers’ digital lives. Here, you can work alongside talented engineers in an open, supportive, inclusive environment where your voice is valued, and you make your own decisions on what tech to use to solve challenging problems. Amex offers a range of opportunities to work with the latest technologies and encourages you to back the broader engineering community through open source. And because we understand the importance of keeping your skills fresh and relevant, we give you dedicated time to invest in your professional development. Find your place in technology on #TeamAmex.

How will you make an impact in this role?

Organizational Context: Senior Development Operations Engineer. Executes end-to-end information security, IT risk management processes and automated capabilities that promote trustworthy AI practices. Works across many organizations, both internal and external, to meet business needs and compliance goals. Leads cross department initiatives, covering a wide range of business/technical functions.

Focus: Responsible for the development and or delivery of AI/ML security initiatives, projects, or programs that have objectives associated with incorporating AI safety controls, adversarial robustness, and stress testing of AI/ML systems Responsible for designing and implementing processes to understand, measure, and improve the organization’s ability to protect AI/ML models, implement AI safety controls for responsible use of LLM (Large Language Model) applications.

 Key Responsibilities

• Implement secure model development life cycle practices with automated white box and black box assessments for AI/ML models.
• Deliver and integrate AI robustness, vulnerability, and stress testing capabilities with MLOps ecosystems.
• Evaluate and assess open-source AI security libraries to build into enterprise AI stress testing and audit capabilities.
• Implement end-to-end LLM security risk management processes and automated protections.
• Incorporate machine learning models into diverse application security tooling and processes.
• Support production deployments of AI/ML safety systems using cloud-native packaging and deployment techniques suchas containers, serverless, CI/CD and APIs.
• Establish and govern AI/ML and Generative AI application security standards.
• Provide production support and operations for AI/ML security systems.
• Manage cloud deployments and automation frameworks in cloud for AI/ML security systems.

Minimum Qualifications

• Bachelor's Degree in Data Science, Statistics, Computer Science or Software Engineering
• 3 Development Operations Experience
• 3 years of software engineering experience
• Certified Kubernetes Administrator (CKA) and Certified Kubernetes Application Developer (CKAD)

Preferred Qualifications:

• Master's Degree - Data Science, Statistics, Computer Science, or Software Engineering 
• Machine Learning Operation Professional Certifications
• Strong knowledge of Adversarial Robustness techniques and tools for machine learning 
• Strong knowledge of AI Risk Management frameworks and Trustworthy AI practices. 
• Hands-on experience with applying statistics, machine learning algorithms (DNN, NLP), big data, and data science toolkits. Hands-on experience designing, implementing, and operationalizing high performant AI/ML pipelines and writing production code.
• Hands-on experience with deploying and operationalizing AI/ML models to public cloud environments.
• Hands-on experience evaluating open-source ML tools, frameworks, and libraries.
• Hands-on experience with commonly used data science programming languages, packages, and tools.
• Hands-on experience with MLOps, DevOps, DataOps and API integrations.
• Hands-on experience with AI workload management.
• Hands-on experience with Cloud architecture, design, implementation, and operations.
• Demonstrated peer reviewed journal publications, conference presentations, open-source contributions, or similar activities