Staff Software Engineer, Globality

• We’re seeking a Staff Software Engineer with deep expertise in back-end development and scalable platform architecture
• This role is ideal for someone passionate about building high-performance systems that enable AI innovation through robust infrastructure, intuitive tooling, and seamless integration of cutting-edge models
• Bachelor’s or Master’s in Computer Science, Engineering, or related field
• 8+ years of experience in back-end software engineering, with proven ability to design and scale distributed systems
• Strong proficiency in Python and Java, with experience in Spring Boot and object-oriented design
• Hands-on experience with AWS cloud services, Docker, and Kubernetes in production environments
• Solid understanding of SQL databases, data modeling, and performance optimization
• Familiarity with microservices architecture, CI/CD pipelines, and infrastructure automation
• Excellent communication and collaboration skills across engineering and product teams

Desirable

• You’ll be at the forefront of operationalizing AI—designing services and data systems that empower teams to build, deploy, and iterate on intelligent applications with speed and reliability
• Platform Architecture & Back-End Development:
• Architect and implement microservices-based platforms with a strong focus on scalability, performance, and reliability. Develop core services using Python, Java, and Spring Boot, and ensure seamless integration with AI and data workflows
• Cloud & Containerization:
• Build and deploy applications on AWS leveraging services for compute, storage, and networking. Implement containerized deployments using Docker and Kubernetes to support CI/CD pipelines and automated scaling
• Data Systems & ETL:
• Collaborate with data engineering teams to design and optimize SQL-based data systems, ETL pipelines, and feature workflows that feed AI models at scale
• Reliability & Observability:
• Drive platform reliability through monitoring, logging, and performance tuning. Implement best practices for CI/CD, automated testing, and infrastructure-as-code
• AI Tooling & Integration:
• Build developer tools and abstractions for integrating LLMs and AI models, including orchestration services and prompting frameworks
• Innovation & Scalability:
• Push the boundaries of platform scalability and performance, ensuring systems can handle large-scale data processing and high-throughput workloads