AI Engineer, Flywire

• Backend Development Expertise: Strong experience in backend development of microservices. Proficiency in Python is strongly preferred
• Cloud Platforms: Hands-on experience with cloud platforms like AWS, particularly with services like RDS and Redis. Experience with Amazon Bedrock is a plus
• CI/CD Pipelines: Experience with continuous integration and continuous deployment (CI/CD) pipelines for both backend services and AI/ML models
• ML Ops Knowledge: Experience in deploying and managing AI/ML models in production, ensuring their scalability and performance

Desirable

• Flywire is seeking a versatile AI Engineer with strong backend development skills to scale our AI-driven solutions and infrastructure
• As an AI Engineer, you’ll join a small, but high-impact team where creativity, resourcefulness, and a willingness to wear many hats are essential
• In this role, you'll bridge the gap between backend systems and advanced AI models, ensuring models are robustly deployed, integrated, and monitored throughout our products
• You’ll collaborate closely with data scientists, software engineers, and DevOps to bring AI initiatives from prototype to production in a secure, scalable, and reliable way
• This role blends backend engineering with AI/ML expertise and is ideal for someone passionate about building world-class AI-powered products at scale
• AI Solution Engineering: Design, implement, and optimize robust systems for deploying and serving AI/ML models (including LLMs and generative AI) across Flywire’s services
• Backend & API Development: Build and maintain APIs (RESTful, real-time, and/or event-based) and backend services and infrastructure for integrating AI/ML into our products
• Model Deployment & Lifecycle Management: Collaborate with data scientists to deploy, monitor, and manage models in production, including versioning, testing, scaling, and retraining
• Cloud & Infrastructure/MLOps: Manage cloud environments (primarily AWS), databases, and develop CI/CD pipelines for robust, scalable AI applications, including monitoring and automation
• System Monitoring & Optimization: Implement monitoring, logging, and health tracking for models and supporting infrastructure to ensure availability, reliability, and performance
• Cross-functional Collaboration: Work with engineers, product managers, and data teams to scope, build, and refine features powered by AI, ensuring alignment with business needs
• Adaptability & Proactiveness: Thrive in a small team environment by pitching in wherever needed, whether that means wrangling data, optimizing an API, debugging infrastructure, or brainstorming with stakeholders