Principal AI Software Engineer

As a Principal AI Software Engineer, you play a crucial role in designing, developing, and integrating AI-first microservices, APIs, and workflows to create intelligent systems for cutting-edge digital banking and financial technology solutions. Your responsibilities include: - Developing scalable, distributed microservices using Java and Python with embedded AI/ML capabilities - Building RESTful and GraphQL APIs for AI-driven features like fraud detection, KYC automation, and personalization - Managing various data types using MySQL and MongoDB - Integrating AI/ML models with tools such as TorchServe, FastAPI, TensorFlow Serving, gRPC, and REST - Implementing real-time pipelines using Apache Kafka and Redis Streams - Aligning DevOps practices with CI/CD tools like Jenkins and GitLab - Leveraging containerization through Docker and Kubernetes - Utilizing AWS services like Lambda, EC2, RDS, S3, ECS, EKS - Developing APIs with OAuth2/JWT authentication to ensure data privacy compliance - Collaborating with cross-functional teams including product managers, QA, and DevOps engineers Your expertise in traditional backend software development, API and microservices design, and AI/ML integration will be valuable in this role. You are expected to have a minimum of 7 years of backend software development experience and proficiency in Java (Spring Boot, Play Framework), Python (for ML integrations and scripting), microservices, and API design. Additionally, experience with MySQL, MongoDB, GraphQL, RESTful API development, secure financial systems, AI/ML model integration in production, Apache Kafka, Redis, AWS, Azure, or GCP services, containerization, orchestration tools, CI/CD pipelines, and modern frontend frameworks is required. Preferred experience includes real-time AI API deployment, AI applications in FinTech such as credit risk and fraud detection, working alongside data scientists and AI researchers, and exposure to Southeast Asia FinTech products. Your role involves close collaboration with AI architects and full-stack teams to embed intelligent automation, real-time analytics, and explainable AI into production environments.,