6 hours ago
London, United KingdomMid Level
Responsibilities
- Design, build, and scale reliable, performant backend systems for enterprise Generative AI products across Scale and customer infrastructure.
- Develop secure, efficient services and APIs that integrate AI models and enterprise data sources.
- Architect distributed systems for data processing, inference, and orchestration of large-scale Generative AI workloads.
- Optimize latency, throughput, and cost across hybrid and multi-cloud AI environments.
- Manage and evolve AWS, Azure, or GCP infrastructure, including automation, observability, and security.
- Collaborate with ML and product teams to productionize GenAI models through APIs, model-serving systems, and evaluation frameworks.
- Improve the reliability, scalability, maintainability, and enterprise readiness of AI systems.
Requirements
- At least 4 years of experience developing large-scale backend or infrastructure systems, particularly distributed services, reliability, and scalability.
- Proficiency in Python or TypeScript and experience designing high-performance APIs and backend architectures with frameworks such as FastAPI, Flask, Express, or NestJS.
- Deep familiarity with cloud infrastructure, preferably AWS and Azure, plus Kubernetes, Docker, and Terraform.
- Experience managing relational and NoSQL databases such as PostgreSQL and DynamoDB and building data-intensive application pipelines.
- Hands-on experience with Generative AI applications, model integration, or AI agent systems, including deploying, evaluating, and scaling AI workloads in production.
- Strong understanding of observability, CI/CD, and security best practices for enterprise or multi-tenant services.
- Ability to balance rapid iteration with production-grade quality and collaborate effectively with ML, infrastructure, and product teams.
Tech Stack
Amazon DynamoDBAWSAzureDockerExpressFastAPIFlaskGoogle Cloud PlatformKubernetesNestJSPostgreSQLPythonTerraformTypeScript
Categories
About Scale AI
Scale AI builds data annotation services and AI development tools for enterprises and government agencies, sold as a platform and managed services. Its products include the Scale Generative AI Platform for building and evaluating agents and the Data Engine for collecting, curating, and labeling training data, including RLHF and model evaluation. Founded in 2016 and headquartered in San Francisco, the company is privately held and works across domains from computer vision to LLM applications.