11 часов назад
Infrastructure Engineer (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Infrastructure Engineer (AI) (ML inference and model serving): Building and scaling production infrastructure that keeps enterprise AI agents reliable and efficient under high concurrency with an accent on model serving, distributed systems, and performance optimization. Focus on designing deployment architecture, resolving infrastructure bottlenecks, and improving latency, throughput, and reliability across ML workloads.
Location: On-site in San Mateo, California, United States
Company
is an early-stage enterprise AI company building a context layer to make AI agents reliable, accurate, and secure for mission-critical business operations.
What you will do
- Own inference and model-serving infrastructure from architecture design through production deployment.
- Build and scale systems that run AI agents reliably and efficiently under high concurrency.
- Collaborate with ML and infrastructure teams on integration and performance optimization.
- Identify infrastructure bottlenecks and lead engineering efforts to resolve them.
Requirements
- 5+ years of experience building and operating ML inference systems, model-serving platforms, or ML infrastructure in production.
- Experience designing and scaling inference-serving infrastructure with TensorFlow Serving, TorchServe, Triton, KServe, or equivalent systems.
- Experience optimizing production ML systems for latency, throughput, and reliability at scale.
- Strong proficiency with Docker and Kubernetes for deploying ML workloads.
- Experience with distributed systems, concurrent requests, resource allocation, monitoring, observability, and debugging.
- Cloud experience with AWS, GCP, or Azure and proficiency in Python, Go, Rust, C++, or Java.
Nice to have
- Experience with knowledge graphs, semantic search, or graph databases such as Neo4j or Amazon Neptune.
- Familiarity with real-time or low-latency inference systems, agentic AI pipelines, or enterprise data infrastructure.
Culture & Benefits
- Hands-on work in an early-stage enterprise AI environment.
- Close collaboration with ML and infrastructure teams.
- Visa sponsorship is not available for this role.
Будьте осторожны: если работодатель просит войти в их систему, используя iCloud/Google, прислать код/пароль, запустить код/ПО, не делайте этого - это мошенники. Обязательно жмите "Пожаловаться" или пишите в поддержку. Подробнее в гайде →