Forward Deployed Engineer - AI/ML Platforms
About The Position
Anyscale is hiring a Forward Deployed Engineer to partner with sophisticated AI organizations and design, deploy, and operate infrastructure for production AI workloads. The role works directly with customer platform, infrastructure, and ML engineering teams to solve complex technical challenges, build scalable AI platforms, modernize ML infrastructure, and operationalize distributed AI applications on Ray and the Anyscale platform. The engineer combines cloud infrastructure expertise with customer engagement skills, serving as a trusted technical advisor and hands-on engineer. They support customers throughout implementation, from architecture and deployment through production operations. Customer feedback from this work also informs the evolution of the Anyscale platform.
Requirements
- 5+ years of experience in cloud infrastructure, platform engineering, DevOps, Site Reliability Engineering, or software engineering.
- Experience building, deploying, or operating ML/AI platforms supporting model training, inference, or large-scale data processing workloads.
- Strong expertise with Kubernetes and containerized production environments.
- Experience operating cloud infrastructure on AWS, Azure, or GCP, including networking, security, IAM, storage, and infrastructure automation.
- Experience with Infrastructure as Code and modern DevOps tooling such as Terraform, Helm, GitOps, CI/CD pipelines, or similar technologies.
- Strong software engineering skills in Python, Go, Java, or a comparable language, with experience building automation or production services.
- Experience working directly with enterprise customers in consulting, professional services, field engineering, solutions architecture, or another customer-facing engineering role.
- Excellent communication skills and ability to work effectively with executive and deeply technical stakeholders.
- Willingness to travel as needed to work alongside strategic customers.
Nice To Haves
- Familiarity with distributed computing frameworks such as Ray, Spark, Dask, or Kubernetes-native distributed systems.
- Passion for solving difficult customer problems and building reusable technical solutions.
Responsibilities
- Design and implement production-grade AI platform architectures on Kubernetes and public cloud infrastructure, including AWS, Azure, and GCP.
- Partner with customer platform, infrastructure, and ML engineering teams to deploy, operate, and optimize distributed AI workloads.
- Lead implementation engagements involving platform installation, networking, security, observability, scaling, upgrades, and operational readiness.
- Troubleshoot distributed systems issues across infrastructure, Kubernetes, networking, storage, and AI applications.
- Develop automation, tooling, reference implementations, and infrastructure-as-code to accelerate customer success and improve repeatability.
- Build relationships with technical leaders, platform teams, and executive stakeholders, translating business objectives into technical solutions.
- Collaborate with Product and Engineering to communicate customer requirements, identify product improvements, and shape platform capabilities.
- Share best practices through technical documentation, architecture guidance, workshops, and enablement.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
