The opportunity
At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning.
What you'll do
Design and implement production-grade AI platform architectures on Kubernetes: and public cloud infrastructure (AWS, Azure, and GCP).
Partner directly with customer platform, infrastructure, and ML engineering: teams to deploy, operate, and optimize distributed AI workloads.
Lead implementation engagements that include platform installation,: networking, security, observability, scaling, upgrades, and operational readiness.
Troubleshoot complex distributed systems issues spanning infrastructure,: Kubernetes, networking, storage, and AI applications.
Develop automation, tooling, reference implementations, and: infrastructure-as-code that accelerate customer success and improve repeatability.
Build trusted relationships with technical leaders, platform teams, and: executive stakeholders, translating business objectives into robust technical solutions.
What they're looking for
- Collaborate closely with Product and Engineering to communicate customer: requirements, identify product improvements, and shape future platform capabilities.
- Share best practices through technical documentation, architecture guidance, workshops, and enablement.
- + years of experience in cloud infrastructure, platform engineering, DevOps,: Site Reliability Engineering, or software engineering.
- Experience building, deploying, or operating ML/AI platforms that support: model training, inference, or large-scale data processing workloads.
