Posted 1d ago

Scale AI Posted 30+ days ago
Software Engineer, Enterprise
Location
London
Contract
Permanent
Full-time
Salary
Not disclosed
Experience
4+ years
Mid-level level
Job summary
Responsibilities
We’re looking for a Backend Engineer to help bring large-scale GenAI systems to production. In this role, you’ll build the core infrastructure that powers AI products for some of the world’s largest enterprises—designing scalable APIs, distributed data systems, and robust deployment pipelines that…
- Design, build, and scale backend systems that power enterprise GenAI products, focusing on reliability, performance, and deployment across both Scale’s and customers’…
- 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.
- Optimize backend performance for latency, throughput, and cost—ensuring AI applications can operate at enterprise scale across hybrid and multi-cloud environments.
- Manage and evolve cloud infrastructure (AWS, Azure, or GCP), driving automation, observability, and security for large-scale AI deployments.
What they're looking for
- 4+ years of experience developing large-scale backend or infrastructure systems, with a strong emphasis on distributed services, reliability, and scalability.
- Proficiency in Python or TypeScript, with experience designing high-performance APIs and backend architectures using frameworks such as FastAPI, Flask, Express, or NestJS.
- Deep familiarity with cloud infrastructure (AWS and Azure preferred), including container orchestration (Kubernetes, Docker) and Infrastructure-as-Code tools like Terraform.
- Experience managing data systems such as relational and NoSQL databases (PostgreSQL, DynamoDB, etc.) and building pipelines for data-intensive applications.
- Hands-on experience with GenAI applications, model integration, or AI agent systems—understanding how to deploy, evaluate, and scale AI workloads in production.
Tech stack
Read the full job posting
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