Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
TL;DR
Software Engineer, Enterprise (AI): Building and scaling backend infrastructure for production-grade Generative AI systems with an accent on distributed data systems, scalable APIs, and robust deployment pipelines. Focus on optimizing performance for latency and throughput and architecting high-load GenAI workloads across hybrid and multi-cloud environments.
Location: London, UK
Company
Scale AI develops reliable AI systems and high-quality data technologies to power the world's leading models for enterprises and governments.
What you will do
- Design and scale backend systems for enterprise GenAI products, ensuring reliability and performance.
- Develop core services and APIs to integrate AI models and enterprise data sources securely.
- Architect distributed systems for data processing, inference, and orchestration of large-scale GenAI workloads.
- Optimize backend performance for latency, throughput, and cost across hybrid and multi-cloud environments.
- Manage and automate cloud infrastructure using AWS, Azure, or GCP via Terraform.
- Collaborate with ML and product teams to bring cutting-edge GenAI models into production.
Requirements
- Must be based in London, UK
- 4+ years of experience developing large-scale backend or infrastructure systems.
- Proficiency in Python or TypeScript with experience in FastAPI, Flask, Express, or NestJS.
- Deep familiarity with cloud infrastructure (AWS and Azure preferred), Kubernetes, Docker, and Terraform.
- Experience managing relational and NoSQL databases such as PostgreSQL and DynamoDB.
- Hands-on experience with GenAI applications, model integration, or AI agent systems.
Culture & Benefits
- Opportunity to work at the forefront of the Generative AI revolution.
- Collaborative environment working with world-leading companies and government agencies.
- Inclusive and equal opportunity workplace committed to diversity.
- High-impact role solving hard infrastructure challenges at enterprise scale.
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