AI Deployment Solutions Architect (RNGD NPU)

FuriosaAI is looking for a Solutions Architect to bring the full potential of our powerful RNGD chips/servers to our customers by acting as the primary technical authority in AI/LLM model deployments. From running POCs to benchmarking and debugging, you will translate RNGD’s powerful system to real-world deployments of customers’ models, empowering customers with FuriosaAI’s powerful solutions. If you are interested in providing the technical expertise in challenging the current status-quo of AI infrastructure in real-world environments, join us in our path to a sustainable future of AI. What You’ll Do Own end-to-end technical enablement for US customers deploying AI models on FuriosaAI's RNGD NPU using the Furiosa SDK Develop POCs, benchmarking studies, and live debugging sessions directly in customer environments Act as the technical authority to the US BD/Sales team during pre-sales and enterprise evaluations; translate deep technical capability into business value for engineering and C-suite audiences Develop deep, current expertise in FuriosaAI's hardware and software stack and demonstrate it at US technical forums, AI conferences, and customer workshops Onboard and train customers on integration patterns, optimization workflows, and best practices post-purchase Serve as a technical feedback loop from US customers back to Seoul HQ product and engineering teams Qualifications 2–5 years in a US customer-facing technical role: Solutions Architect, Sales Engineer, Forward Deployed Engineer, or equivalent at an AI infra, cloud, or semiconductor company Actively current on the AI/LLM landscape — tracking model releases, inference frameworks, and serving stack evolution in real time Hands‑on experience with modern inference stacks: vLLM, SGLang, TensorRT‑LLM, Triton Inference Server, or similar Hands‑on experience with agent and orchestration frameworks: LangChain, LlamaIndex, LangGraph, AutoGen, or MCP‑based tooling Proficiency in Python; comfortable with DNN frameworks (PyTorch, TensorFlow) Strong written and verbal communication — able to engage credibly with ML engineers at frontier labs and VP/C‑suite executives Authorized to work in the US; able to travel to customer sites and to Seoul HQ periodically Preferred Qualifications Prior experience at a US AI chip company, cloud silicon team, or AI infrastructure startup Familiarity with NPU/GPU accelerator ecosystems, PCIe integration, and data center hardware deployment Experience with inference optimization: quantization, kernel tuning, batching strategies, memory bandwidth optimization Proficiency in C, C++, or Rust Experience working with distributed or cross‑timezone engineering teams #J-18808-Ljbffr