Back
Niche ML-infrastructure specialization tempered by metro location and mid-level seniority driving medium competition.
Highly domain-specific AI infrastructure and GPU expertise limits cross-industry transferability.
Explicit 4+ years requirement plus mandatory GPU, Kubernetes, and ML infra expertise increases screening rigidity.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead deployment, optimization, and scaling of production AI-native and agentic workloads on DigitalOcean's AI-Native Cloud for strategic enterprise customers.
Operate as the primary technical liaison across product engineering, AI infrastructure, and customer implementation, including building reusable deployment frameworks, automation tooling, and benchmarking systems.
Act as early adopters of AI platform capabilities, validating products with real workloads and providing feedback to drive platform maturity and customer adoption.
4+ years in Forward Deployed Engineering, ML Engineering, Applied AI, AI Infrastructure, Technical Consulting, or equivalent customer-facing roles supporting production AI systems.
Strong hands-on experience with inference and serving frameworks like vLLM, SGLang, Ray Serve, NVIDIA Dynamo, or equivalent including LLM optimization techniques.
Deep expertise with NVIDIA/AMD GPU platforms and ecosystems (CUDA, ROCm, TensorRT, Triton, NCCL, RCCL, NVLink, etc.) and proficiency in Kubernetes, distributed systems, networking, and Infrastructure as Code.
Ability to travel up to 30% and availability to overlap with North American business hours until at least noon ET. Location: Bengaluru, India.
Experienced in architecting and operationalizing large-scale, production AI inference and runtime systems with a focus on scalability, reliability, and cost-efficiency.
Skilled at cross-functional collaboration with CTOs, principal architects, product engineering, and ecosystem partners to drive adoption and platform enhancements.
Expert in building technical enablement assets (playbooks, deployment frameworks, automation tooling) and comfortable engaging with GPU vendors, infrastructure providers, and AI ecosystem partners.