Senior Software Engineer II

We are seeking an experienced and skilled passionate Senior Kotlin Microservices Engineer with AI engineering exposure and a curious, problem-solving mindset to help build a global platform that will serve as a cornerstone of American Express’s AML regulatory compliance ecosystem.

Our platform is designed as a suite of microservices, leveraging both synchronous APIs and asynchronous messaging, alongside a modern Angular-based user interface. This architecture enables us to continuously evolve our technology stack and adopt innovative solutions. While Kotlin and Kafka sit at the heart of our ecosystem, we’re looking for someone who is comfortable working across a broader set of technologies—including AI/ML-powered services, data pipelines, and intelligent automation frameworks—and can quickly adapt to new tools and ideas.

In addition to core backend engineering, you will contribute to the design and integration of AI-driven capabilities, such as risk scoring models, anomaly detection, and intelligent decisioning systems that enhance AML/KYC processes. Experience or interest in working with machine learning models, LLM APIs, feature engineering, or model deployment (MLOps) will be highly valuable. The role combines greenfield development with integration into existing enterprise systems, including embedding AI services into scalable microservice architectures.

As part of the India-based KYC/AML Technologies team, you will play a key role in delivering innovative solutions that meet critical business needs. We operate as an agile, collaborative team, working closely with business stakeholders and taking shared ownership of delivering high-quality, resilient software. This includes full responsibility across the delivery lifecycle—from design to deployment and continuous improvement—including monitoring and optimizing AI model performance in production environments.

This is an excellent opportunity to work within a diverse, global organisation that offers strong career growth, whether along a technical or leadership path. While you will be aligned to the Know Your Customer (KYC) Scrum Team, you will also have opportunities to contribute across the wider portfolio, including advancing the organisation’s AI adoption strategy within compliance and financial crime domains.

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.

Key Responsibilities

• Design and develop greenfield microservices using a containerised, cloud-based architecture (private and public cloud), primarily with Kotlin and Kafka
• Integrate with enterprise platforms, including the Amex Lumi Big Data ecosystem
• Build and consume APIs in line with Amex standards (e.g. One Data, EAG, Apigee, EWP, and API security frameworks)
• Collaborate with data scientists and ML engineers to productionise models, including building scalable inference services and supporting MLOps practices (model deployment, monitoring, and lifecycle management)
• Leverage LLMs and AI APIs where appropriate to enhance KYC/AML workflows, such as document analysis, entity resolution, and case investigation support
• Develop and maintain data pipelines and feature services to support machine learning use cases, ensuring data quality, consistency, and governance
• Diagnose and resolve issues through debugging and log analysis, while improving observability via monitoring, alerting, and self-healing capabilities, including tracking performance and drift of AI models in production
• Actively contribute to agile ceremonies and promote a culture of continuous improvement and engineering excellence, including identifying opportunities to apply AI-driven automation and intelligence across the platform

Qualifications & Experience

• Proven ability to write clean, well-structured, and thoroughly tested code, including leveraging AI-assisted development tools (e.g. GitHub Copilot, Devin or similar) to improve productivity, code quality, and documentation
• Strong experience with Kotlin, Spring, and familiarity with other JVM languages such as Java or Groovy
• Solid understanding of development tooling, including version control, build automation, and ideally containerisation and orchestration technologies
• Experience with relational databases, big data platforms (Lumi), API design, and messaging systems—preferably Apache Kafka
• Experience using AI tools to support testing and quality engineering, including generating test cases, improving coverage, and validating edge cases in complex systems
• Ability to critically evaluate and validate AI-generated code and tests, ensuring correctness, security, and maintainability in production systems
• Knowledge of application and data security, including securing data in transit and at rest, and mitigating common vulnerabilities
• Experience working in Unix/Linux environments
• Experience monitoring and optimising distributed systems performance, including services augmented with AI components (e.g. inference latency, throughput, cost efficiency)
• Familiarity with observability and monitoring, including logging, metrics, alerting, and detecting issues such as model drift or degradation
• Exposure to building or integrating AI agents or agentic workflows, including orchestrating multi-step tasks, tool usage, and API interactions within backend systems
• Understanding of responsible AI usage in engineering workflows, including data privacy, security, and governance considerations when using AI-assisted coding tools
• Bachelor’s degree in a relevant field or equivalent practical experience