Solutions Architect, LLM Model Builder

What you'll do

• Serve as the lead technical advisor for partners delivering reasoning, multimodal, and model-serving solutions.
• Guide partners through fine-tuning, distillation, quantization, compression, and evaluation workflows.
• Define benchmark plans, synthetic data generation, and repeatable validation recipes for foundation models.
• Advise on compute planning including cluster sizing, GPU/network selection, and memory tradeoffs.
• Design inference architectures focusing on prefill/decode tradeoffs, batching, routing, and serving efficiency.
• Develop reference architectures, playbooks, TCO calculators, and sizing models using NVIDIA's software stack.
• Translate complex model and infrastructure topics for both technical teams and executive stakeholders.

What we're looking for

• Master's or PhD in Computer Science, Electrical Engineering, Software Engineering, ML Engineering, or a related field (or equivalent experience).
• 5+ years of relevant experience working with LLMs, VLMs, and large-scale inference systems.
• Hands-on expertise in fine-tuning, benchmarking, evaluation, optimization, and production deployment as a Research Engineer or Deep Learning Engineer.
• Strong understanding of foundation models across data preparation, post-training, and inference.
• Familiarity with reasoning models, reinforcement learning, and synthetic data generation workflows.
• Strong programming skills in Python and hands-on experience with PyTorch, JAX, or TensorFlow.
• Familiarity with Nemotron, NeMo, Dynamo, TensorRT-LLM, Triton, vLLM, and similar inference/optimization stacks.
• Strong communication and presentation skills to advise both technical teams and executives.