Senior Forward Deployed Engineer I (AI Infra)

Position Overview

We are looking for a Senior Forward Deployed Engineer I (FDE) who is passionate about operationalizing and collaborating closely with strategic AI enterprises and high-growth startups to architect, implement, and fine-tune production infrastructure across DigitalOcean's AI-Native Cloud.

As an AI Infrastructure Engineer within the Forward Deployed Engineering team, you sit at the intersection of deep systems engineering and high-impact customer architecture. You won't just build infrastructure in a vacuum; you will embed directly with customer engineering teams to solve complex infrastructure bottlenecks, optimize heterogeneous GPU cluster performance, and engineer mission-critical inference and training platforms.

If you thrive on squeezing maximum compute and memory throughput out of modern GPU clusters-whether optimizing NVIDIA (H100 /H200/B200/B300) or leveraging high-capacity AMD Instinct (MI300X/MI325X) hardware-and debugging low-level distributed stacks from drivers to orchestration, this is your playground.

Your mission is to accelerate production adoption of AI-native systems while helping shape the future of DigitalOcean's AI-Native Cloud for the inference and agentic era.

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.

What You'll Add to DigitalOcean

• Cloud & Orchestration: Expertise with Linux systems engineering, Kubernetes, and Infrastructure as Code (Terraform, Helm).

Heterogeneous GPU & Acceleration Stack: Hands-on experience managing NVIDIA Stack (CUDA, NCCL, NVLink, and Triton Inference Server ) Or AMD Stack ( ROCm RCCL, CDNA)

• Inference & Distributed AI: Experience with modern LLM serving frameworks (vLLM, TensorRT-LLM, Ray Serve) running on both CUDA and ROCm backends.
• Networking & Storage: Deep understanding of high-performance interconnects (RDMA, InfiniBand, RoCE, AMD Infinity Fabric) and high-throughput storage systems suited for massive datasets (e.g., Ceph, Lustre, NVMe-oF).
• Programming: Strong proficiency in Python and Go (C++, CUDA C/C++, or AMD HIP is a major plus).

Preferred Qualifications

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

JR: 2026-7937

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