Software Engineer (Enterprise)

Join Scale, a leading company in the AI revolution. As a Backend Engineer, you will build the core infrastructure that powers AI products for large enterprises. You will design scalable APIs, distributed data systems, and robust deployment pipelines, ensuring production-grade reliability and performance. This is a unique opportunity to shape the future of enterprise AI and directly impact how businesses operate and innovate.

• Design, build, and scale backend systems that power enterprise GenAI products, focusing on reliability, performance, and deployment across both Scale’s and customers’ infrastructure.
• Develop core services and APIs that integrate AI models and enterprise data sources securely and efficiently, enabling production-scale AI adoption.
• Architect scalable distributed systems for data processing, inference, and orchestration of large-scale GenAI workloads.
• If you're excited about pushing the boundaries of AI-driven product development, we want to hear from you
• Ability to balance rapid iteration with production-grade quality, shipping reliable backend systems in fast-paced environments
• Deep familiarity with cloud infrastructure (AWS and Azure preferred), including container orchestration (Kubernetes, Docker) and Infrastructure-as-Code tools like Terraform
• Proficiency in Python or TypeScript, with experience designing high-performance APIs and backend architectures using frameworks such as FastAPI, Flask, Express, or NestJS
• Collaborative mindset, working closely with ML, infra, and product teams to bring complex GenAI systems into production at enterprise scale
• Experience managing data systems such as relational and NoSQL databases (PostgreSQL, DynamoDB, etc.) and building pipelines for data-intensive applications
• 4+ years of experience developing large-scale backend or infrastructure systems, with a strong emphasis on distributed services, reliability, and scalability
• Strong understanding of observability, CI/CD, and security best practices for running services in enterprise or multi-tenant environments
• Hands-on experience with GenAI applications, model integration, or AI agent systems—understanding how to deploy, evaluate, and scale AI workloads in production