Senior AI Engineer

Senior AI Engineer

Senior Software Engineer (Generative AI) - Foundry R&D, Singapore

• Develop backend services for AI features: Build and maintain scalable APIs and microservices in Java (Spring Boot) or Python that expose our generative AI capabilities - routing user queries to LLMs and other models, processing data, and returning results securely and efficiently.


• Integrate generative AI technologies: Partner with data science and ML engineering to productionise models - wrapping them in reliable service interfaces, managing input/output formats, adding supporting data flows such as external API calls and response caching, and ensuring they scale.


• Ensure performance and reliability: Own the quality of the services you build through unit and integration tests, profiling and removing bottlenecks, and monitoring and alerting (CloudWatch, Prometheus). Troubleshoot production issues and keep improving logging and observability.


• Collaborate and iterate cross-functionally: Work in an agile team with product managers, designers, and data scientists to turn requirements into well-designed APIs, and refine features iteratively as models and interfaces evolve.


• Mentor and uphold best practices: Guide junior engineers through thorough code reviews, champion clean and maintainable design, and improve our tooling, CI practices, and documentation.

• Strong backend engineering experience: 5+ years building backend systems and APIs, with expertise in Java (Spring Boot) or Python (Django/FastAPI) and a track record of efficient, scalable, modular server-side code.


• AI/ML integration experience: Hands-on work on AI or data-intensive applications - calling AI APIs, integrating pre-trained models, or productionising ML with data science teams - plus genuine enthusiasm for generative AI and large language models.


• API and database proficiency: Skilled in RESTful API design (versioning, authentication, documentation), data modelling, and complex SQL. Familiarity with NoSQL stores, caches, or message queues is a plus.


• Quality-focused and detail-oriented: Disciplined about unit, integration, and end-to-end testing (JUnit, PyTest), robust error handling, safe logging, and edge cases such as unusual model outputs or slow downstream services.


• Problem-solving and adaptability: Systematic debugging of complex systems using profilers, debuggers, and log analysis, and the flexibility to learn quickly and keep delivering as R&D priorities shift.


• Collaboration and communication: Able to explain technical trade-offs to non-technical colleagues, contribute constructively to design discussions and code reviews, and raise concerns or clarify requirements early.

• Education and background: Bachelor's degree in Computer Science, Engineering, or a similar field, and 5+ years as a software engineer focused on backend or full-stack development in agile teams, shipping products involving high volumes, heavy data processing, or third-party integrations.


• Back-end programming mastery: Advanced skill in at least one back-end language (Java, Python, Go) and its common frameworks, comfort with multi-threading or async programming, Git-based collaborative workflows, and command-line scripting.


• Web services and microservices: Deep understanding of and REST, experience with microservices communicating via REST or message queues (Kafka, RabbitMQ), and familiarity with API gateways, load balancers, and tools such as Postman or Swagger.


• Database and data management: Proficiency writing and optimising SQL (joins, indexing, transactions), sound schema design, ORM experience (Hibernate, SQLAlchemy), and familiarity with at least one NoSQL or caching solution (Redis, MongoDB).


• Cloud and CI/CD: Experience deploying backend services on AWS, Google Cloud, or Azure, containerisation with Docker, and automated pipelines (Jenkins, GitLab CI, GitHub Actions). Kubernetes or serverless experience is a plus.


• AI services and frameworks (basic exposure): Familiarity with AI/ML concepts or APIs - for example calling an NLP service, running a model with TensorFlow/PyTorch, or integrating the OpenAI API - and comfort handling auth tokens, JSON responses, and model outputs.


• Testing and monitoring: Proven ability to build comprehensive test suites with mocked external services, and to set up health checks, dashboards, and alerts using tools such as Grafana, New Relic, or Datadog.


• Agile and teamwork: Experience in an Agile/Scrum environment, breaking down user stories and delivering within sprints, using tools such as JIRA, and communicating clearly in English across geographically distributed teams.

• Generative AI familiarity: Experience with GPT models, fine-tuning transformers, prompt engineering, Hugging Face libraries, or vector databases and embeddings.


• Performance optimisation: A track record of cutting API latency, scaling systems for far higher load, streaming model outputs, or batching requests for throughput.


• DevOps and automation: Terraform or Helm, infrastructure as code, automated scaling policies, or hands-on Kubernetes deployment configuration.


• Full-stack exposure: Some front-end experience (React, Angular, or mobile) that helps you design developer-friendly APIs and build quick internal tools or dashboards.


• Domain knowledge: Interest or experience in payments, finance, or commerce that helps contextualise the use cases our generative AI projects target.


• Continuous learning and initiative: Relevant certifications, open-source contributions, or personal projects that show curiosity and drive.


• Achievements and leadership: Informal technical leadership - being the go-to person for a system, leading a major refactor, or driving a successful hackathon project - showing you can take ownership and grow into larger responsibilities.

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;


• Ensure the confidentiality and integrity of the information being accessed;


• Report any suspected information security violation or breach, and


• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.