Senior Forward Deployed Engineer I (AI Infra)

About this role

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Skills you'll use

What you'll do

• Embed & Execute: Act as the primary technical authority on heterogeneous AI infrastructure for high-value DigitalOcean customers, co-engineering custom GPU infrastructure solutions for their production workloads.Optimize Multi-Vendor AI Pipelines: Architect and fine-tune low-latency, high-throughput LLM serving platforms across NVIDIA CUDA Or AMD ROCm™ platforms using serving frameworks (e.g., vLLM, TensorRT-LLM, SGLang, TGI) and model execution techniques (quantization, KV caching, speculative decoding).Cluster Orchestration & SRE: Deploy, scale, and manage resilient Kubernetes clusters (DOKS/Bare Metal) tailored for compute-heavy AI workloads, utilizing tools like Ray, Slurm, and KubeFlow.Infrastructure as Code: Build scalable, repeatable blueprints using Terraform, Ansible, and Helm to automate multi-vendor GPU provisioning, high-speed networking, and storage stacks for customer deployments.Low-Level Heterogeneous Troubleshooting: Debug complex stack issues spanning host drivers (NVIDIA CUDA / AMD ROCm, HIP), container runtimes, inter-GPU communication libraries (NCCL / RCCL), high-speed interconnects (InfiniBand / RoCE / Infinity Fabric™), and distributed storage systems.Build for Scale: Translate common customer infrastructure challenges into core platform features, working directly with DigitalOcean’s product and core infrastructure teams to refine our cloud offering.Travel & Collaboration Requirements: Ability to travel up to 30% for customer engagements, strategic workshops, conferences, and internal collaboration. Ability to consistently overlap with North American business hours, including availability until at least noon Eastern Time, to collaborate effectively with customers, Product, Engineering, and go-to-market teams.

Who can apply

• AI Infrastructure & Forward Deployed Engineering Experience: 6+ years of experience working in Forward Deployed Engineering, AI Infrastructure, Technical Consulting roles supporting production AI systems.
• Customer Empathy & Technical Leadership: Ability to translate complex infrastructure concepts to engineering solutions and collaborate directly with client teams (CTOs, AI Leads).
• Builder Mentality: Preference for delivering production-ready code, low-latency container images, and deployment blueprints over slide decks.
• Agility: Comfortable navigating fast-moving environments and tuning model workloads for diverse accelerator architectures.
• Vendor & Strategic Partnership Collaboration: Experience collaborating with GPU vendors, infrastructure providers, model vendors, or ecosystem partners on benchmarking, optimization, technical validation, or launch readiness initiatives.
• *This job is located in Bengaluru, India

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