Software Engineer, Enterprise

Scale AI; City Of London; Presencial; GBP 85 000 - 110 000; Tempo integral

Resumo da oferta

Scale AI is seeking a Backend Engineer to build scalable GenAI infrastructure for enterprise customers. You will design and implement robust APIs, distributed data pipelines, and deployment workflows that run at production scale across hybrid cloud environments.

You will collaborate with ML and product teams to integrate AI models, ensure reliability, security, and observability, and ship high-quality backend systems that empower Scale’s GenAI platform in enterprises.

Qualificações

• 4+ years of experience developing large-scale backend or infrastructure systems.
• Proficiency in Python or TypeScript with experience designing high-performance APIs.
• Deep familiarity with cloud infrastructure (AWS and Azure) including container orchestration and Terraform.
• Experience managing data systems (PostgreSQL, NoSQL) and building data pipelines.
• Hands-on experience with GenAI applications, model integration, or AI agent systems.
• Strong understanding of observability, CI/CD, and security for enterprise services.
• Ability to balance rapid iteration with production-grade quality.

Responsabilidades

• Design, build, and scale backend systems powering enterprise GenAI products with reliability and performance.
• Develop core services and APIs that securely integrate AI models and data sources.
• Architect scalable distributed systems for data processing, inference, and orchestration of GenAI workloads.
• Optimize backend performance for latency, throughput, and cost across hybrid/multi-cloud environments.
• Manage and evolve cloud infrastructure (AWS, Azure, or GCP) with automation and observability.
• Collaborate with ML and product teams to deploy and scale AI workloads in production.
• Improve reliability and scalability through robust engineering practices.

Conhecimentos

Python

TypeScript

Distributed systems

Cloud infrastructure

Kubernetes

Docker

Express

NestJS

FastAPI

Terraform

PostgreSQL

DynamoDB

CI/CD

Observability

Ferramentas

FastAPI

Flask

Express

NestJS

Terraform

Docker

Kubernetes

Descrição da oferta de emprego

At Scale AI, we're not just building AI tools-we're pioneering the next era of enterprise AI. As businesses race to harness the power of Generative AI, Scale is at the forefront, delivering cutting‑edge solutions that transform workflows, automate complex processes, and drive unparalleled efficiency for the largest enterprises. Our Scale Generative AI Platform (SGP) provides foundational services and APIs, enabling businesses to seamlessly integrate AI into their operations at production scale.

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 enable production‑grade reliability and performance.

This is a rare opportunity to be at the center of the GenAI revolution, solving hard backend and infrastructure challenges that make AI truly work at enterprise scale. If you're excited about shaping how AI systems are deployed and scaled in the real world, we want to hear from you.

At Scale, we don't just follow AI advancements - we lead them. Backed by deep expertise in data, infrastructure, and model deployment, we are uniquely positioned to solve the hardest problems in AI adoption. Join us in shaping the future of enterprise AI, where your work will directly impact how businesses operate, innovate, and grow in the age of GenAI.

You Will:

• 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.
• 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.
• Collaborate with ML and product teams to bring cutting‑edge GenAI models into production through efficient APIs, model serving systems, and evaluation frameworks.
• Continuously improve reliability and scalability, applying strong engineering practices to make AI systems robust, maintainable, and enterprise‑ready.

Ideally, You Have:

• 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.
• Strong understanding of observability, CI/CD, and security best practices for running services in enterprise or multi‑tenant environments.
• Ability to balance rapid iteration with production‑grade quality, shipping reliable backend systems in fast‑paced environments.

Collaborative mindset, working closely with ML, infra, and product teams to bring complex GenAI systems into production at enterprise scale.

PLEASE NOTE:Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high‑quality data and full‑stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at . Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision.

PLEASE NOTE:We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.