Solutions Architect, LLM Model Builder

Serve as the lead technical advisor for partners delivering reasoning, multimodal, fine-tuning, and model-serving solutions.

Guide partners to the right approach for customer workloads across fine-tuning, distillation, quantization, compression, benchmarking, and evaluation.

Define benchmark plans, synthetic data and evaluation workflows, and repeatable validation recipes.

Advise on compute planning, including cluster sizing, GPU and network selection, storage, memory tradeoffs, latency and throughput targets, and production-readiness testing.

Guide inference architecture across prefill and decode tradeoffs, batching, routing, disaggregated inference, and serving efficiency.

Develop reference architectures, playbooks, benchmark recipes, TCO calculators, and sizing models across CUDA, NeMo, Nemotron, Dynamo, TensorRT-LLM, Triton, NIMs, and related tooling.

Support pre- and post-sales engagements by translating complex model and infrastructure topics for partner and customer teams.

MSc, PhD in Computer Science, Electrical Engineering, Software Engineer, ML Engineer, or related fields (or equivalent experience).

5+ years of relevant experience working with LLMs, VLMs, and large-scale inference systems, with hands-on expertise in fine-tuning, benchmarking, evaluation, optimization, and production deployment as a Research Engineer, Deep Learning Engineer, or equivalent.

Strong understanding of foundation models across data preparation, fine-tuning, post-training, evaluation, and inference.

Familiarity with reasoning models, reinforcement learning, and synthetic data generation and evaluation 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 and optimization stacks.