1 day ago
Base Salary
$250k - $275k/yr
Responsibilities
- Design and implement production AI platform architectures on Kubernetes and AWS, Azure, and GCP.
- Deploy, operate, and optimize distributed AI workloads with customer platform, infrastructure, and ML engineering teams.
- Lead implementation engagements covering installation, networking, security, observability, scaling, upgrades, and operational readiness.
- Troubleshoot distributed systems issues involving infrastructure, Kubernetes, networking, storage, and AI applications.
- Develop automation, tooling, reference implementations, and infrastructure-as-code for repeatable customer deployments.
- Build relationships with technical and executive stakeholders and translate business objectives into technical solutions.
- Collaborate with Product and Engineering to communicate customer requirements and influence platform improvements.
- Create technical documentation, architecture guidance, workshops, and enablement materials.
Requirements
- At least 5 years of experience in cloud infrastructure, platform engineering, DevOps, Site Reliability Engineering, or software engineering.
- Experience building, deploying, or operating ML/AI platforms for model training, inference, or large-scale data processing.
- Strong expertise with Kubernetes and containerized production environments.
- Experience operating AWS, Azure, or GCP infrastructure, including networking, security, IAM, storage, and infrastructure automation.
- Experience with infrastructure-as-code and tooling such as Terraform, Helm, GitOps, and CI/CD pipelines.
- Strong software engineering skills in Python, Go, Java, or a comparable language, including automation or production services experience.
- Experience working directly with enterprise customers in consulting, professional services, field engineering, solutions architecture, or another customer-facing engineering role.
- Excellent communication skills with executive and deeply technical stakeholders.
- Familiarity with distributed computing frameworks such as Ray, Spark, Dask, or Kubernetes-native distributed systems.
- Willingness to solve difficult customer problems and build reusable technical solutions.
Benefits
- Travel as needed to work alongside strategic customers.
Categories
Forward Deployed
About Anyscale
Anyscale builds a cloud platform and tools to run Ray, the open-source framework for distributed Python and AI/ML workloads, enabling teams to scale data prep, training, and inference. It monetizes through a managed service, enterprise features, and support for Ray deployments. Founded in 2019 and headquartered in San Francisco, this privately held company is the commercial steward of Ray, widely used to power production AI systems.