Backend Engineer (AI/ML Infrastructure)
Вакансия напрямую с сайта из расширенного списка "глобальных компаний для русскоязычных специалистов" - туда входят компании с восточно-европейскими корнями.
Обычно нужен английский ~B2 и локация вне РФ/РБ (и/или ИП). Может требовать VPN для доступа
Описание вакансии
CloudGeometry is a leading cloud-native engineering firm delivering high-impact solutions across infrastructure, machine learning, and intelligent applications.
Overview
The company is looking for AI Infrastructure Engineers to join the team supporting large-scale AI/ML systems. This is a hands-on engineering role focused on building scalable, secure, and production-ready infrastructure that powers ML workflows end-to-end—from experimentation to deployment and monitoring. You’ll work alongside top-tier engineers across the US, LATAM, and Europe on cutting-edge projects in AI, cloud, and enterprise SaaS.
What you will do
- Design, implement, and maintain robust infrastructure for ML workflows across real-time and batch environments.
- Build and support production-grade model lifecycle systems.
- Develop APIs and backend services in TypeScript and Python to support model integration and orchestration.
- Manage and optimize infrastructure using AWS and infrastructure-as-code (CDK preferred).
- Collaborate with cross-functional teams to deliver high-impact features.
- Monitor and improve infrastructure reliability, security, and performance across diverse deployment targets.
Requirements
- 7+ years in software or infrastructure engineering with proven experience supporting AI/ML systems.
- Deep hands-on experience with AWS services and modern IaC practices (Terraform/CDK).
- Strong backend programming skills in TypeScript and Python.
- Production-level use of MLFlow for model management and deployment.
- Expertise in containerization (Docker), CI/CD automation, and orchestration tools.
- Solid understanding of designing scalable and secure systems in cloud-native environments.
Nice to have
- Exposure to LLM infrastructure and frameworks (e.g., DSPy, LangChain).
- Knowledge of LLM performance metrics: latency, cost monitoring, and usage optimization.
- Familiarity with semantic search tools and vector stores (e.g., OpenSearch, Pinecone).
Culture & Benefits
- Remote anywhere
- Coworking space financial coverage
- Flexible working hours
- B2B with multiple benefits
- Workspace program: 2500$ for work equipment of your choice.
- Paid courses and certifications