Agentic AI Engineer II - Cyber Analytics

Joining Amex Tech means discovering and shaping your contribution to something big. Here, you can work alongside talented tech teams and build a unique career with the Powerful Backing of American Express. With a range of opportunities to work with the latest technologies, and a commitment to back the broader engineering community through open source, our mission is to power your success. Because Amex Tech is powered by our technology, our culture, and our colleagues.

Data Analytics Solutions - Emerging Technologies team is looking for an Engineer - Artificial Intelligence to help design and build impactful, intelligent, and secure AI solutions for Information Security & Governance and Control. We are seeking a technically strong AI professional with hands-on experience in Machine Learning, Deep Learning, Generative AI, and AI system development-someone who can translate business needs into scalable, production-ready AI applications and contribute to solutions that drive meaningful outcomes.

If you are excited to work at the intersection of AI, data, cybersecurity, and emerging technologies to solve complex business challenges, this role is for you.

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 our 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.

• Bachelor''s degree in computer science, Engineering, Data Science, AI, or a related field.
• Hands-on experience developing AI/ML, GenAI, or data-driven applications.
• Strong Python programming skills and familiarity with frameworks such as PyTorch, TensorFlow, scikit-learn, or similar tools.
• Working knowledge of LLMs, NLP, embeddings, RAG, prompt engineering, and AI application development.
• Exposure to APIs, cloud platforms, Docker, Kubernetes, vector databases, and software engineering best practices.
• Ability to understand business needs, solve technical problems, and collaborate effectively across teams.

Preferred Qualifications

• Exposure to Information Security, Governance, Technology Risk, cybersecurity, or enterprise risk analytics.
• Familiarity with agentic AI frameworks such as LangChain, AutoGen, CrewAI, or similar tools.
• Exposure to MLOps/data tools such as MLflow, Kubeflow, Argo Workflows, Kafka, Spark, or NiFi.

Hands-on experience or working knowledge of the following tools, frameworks, and technologies:

• Data Engineering: SQL, NoSQL, Spark, Kafka, NiFi.
• AI/ML Development: Python, TensorFlow, PyTorch, scikit-learn, Jupyter Notebooks, Shell Scripting, and vector databases such as pgVector or Milvus.
• MLOps & Orchestration: MLflow, Kubeflow Pipelines, Argo Workflows, ML Run, or Triton Inference Server.
• Deployment & Containers: Docker, Kubernetes, RESTful APIs development.
• Observability & CI/CD: Elasticsearch, Kibana, Prometheus, Jenkins, XL Release, or similar tools.
• Cloud: Exposure to AWS, Azure, or Google Cloud is a plus.
• 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.

As an AI Engineer, you will play a key role in designing, developing, and deploying end-to-end AI applications that solve complex business challenges and enable intelligent automation. This role requires strong hands-on experience in Machine Learning, Deep Learning, and Generative AI, along with practical exposure to building AI-driven systems, including agent-based solutions. You will work in a cross-functional environment that values innovation, engineering rigor, secure solution design, and responsible AI development.

• Build and enhance AI/ML and GenAI-powered solutions using Python, LLMs, RAG, prompt engineering, and agentic AI frameworks.
• Translate business, Information Security, Governance, and Technology Risk requirements into scalable AI applications.
• Develop, test, and improve models for NLP, classification, clustering, anomaly detection, and predictive analytics.
• Support model lifecycle activities including experimentation, evaluation, versioning, deployment, monitoring, and documentation.
• Contribute to cloud-native ML pipelines and AI application deployment using MLOps, APIs, Docker, Kubernetes, and related tools.
• Collaborate with Security, Risk, Engineering, Governance, and Data teams to deliver secure, reliable, and responsible AI solutions.
• Stay current with emerging trends in Machine Learning, Generative AI, Agentic AI, MLOps, and responsible AI.

As an AI Engineer, you will play a key role in designing, developing, and deploying end-to-end AI applications that solve complex business challenges and enable intelligent automation. This role requires strong hands-on experience in Machine Learning, Deep Learning, and Generative AI, along with practical exposure to building AI-driven systems, including agent-based solutions. You will work in a cross-functional environment that values innovation, engineering rigor, secure solution design, and responsible AI development.

• Build and enhance AI/ML and GenAI-powered solutions using Python, LLMs, RAG, prompt engineering, and agentic AI frameworks.
• Translate business, Information Security, Governance, and Technology Risk requirements into scalable AI applications.
• Develop, test, and improve models for NLP, classification, clustering, anomaly detection, and predictive analytics.
• Support model lifecycle activities including experimentation, evaluation, versioning, deployment, monitoring, and documentation.
• Contribute to cloud-native ML pipelines and AI application deployment using MLOps, APIs, Docker, Kubernetes, and related tools.
• Collaborate with Security, Risk, Engineering, Governance, and Data teams to deliver secure, reliable, and responsible AI solutions.
• Stay current with emerging trends in Machine Learning, Generative AI, Agentic AI, MLOps, and responsible AI.