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Specialized AI infrastructure role with mid-level experience in a metro hub and a known cloud brand.
Role requires niche AI-infrastructure, GPU, and inference expertise, so cross-industry transferability is limited.
Explicit 6+ years and deep mandatory GPU, inference, orchestration, and deployment experience makes filters strict.
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Serve as the technical lead for deploying, optimizing, and scaling production AI and agentic workloads on DigitalOcean's AI-Native Cloud for strategic customers.
Validate and provide operational feedback on new AI-native platform capabilities across inference engines, runtimes, orchestration systems, and GPU infrastructure to accelerate product maturity.
Build scalable deployment frameworks, automation tooling, benchmarking systems, and AI starter kits to enable broader platform adoption and operational excellence.
6+ years experience in Forward Deployed Engineering, ML Engineering, AI Infrastructure, Technical Consulting, or similar roles supporting production AI systems.
Hands-on expertise with AI inference and serving frameworks (e.g., vLLM, SGLang, Ray Serve), LLM optimization techniques, and GPU platforms (NVIDIA/AMD) including CUDA, ROCm, TensorRT, Triton.
Strong programming skills in Python or Go for building tooling, automation, and operational platforms.
Ability to travel up to 30% and overlap with North American business hours, including availability until at least noon Eastern Time.
Demonstrated success in managing production-scale AI workloads across inference, runtime, orchestration, and agentic application layers with focus on scalability, latency, and cost optimization.
Experience collaborating cross-functionally with strategic customers, product engineering, GPU vendors, and ecosystem partners to influence AI platform development and adoption.
Proven ability to act as a bridge between technical product teams and customers, converting real-world operational insights into scalable deployment standards and product improvements.