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Senior Solutions Architect – Diffusion AI Models
NVIDIASenior Solutions Architect helping EMEA AI companies deploy and optimize NVIDIA diffusion models. Improving image, video, and multimodal generation performance across production pipelines.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI/ML and computer vision, with a focus on diffusion model architectures and video generation techniques. Proficient in optimizing generation pipelines and providing technical guidance on NVIDIA's inference stack.
Highest-signal resume keywords
Diffusion Model FrameworksComputer Vision ModelsNVIDIA Inference StackVideo Generation TechniquesStrong Communication Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Image GenerationVideo GenerationDiffusion Model ArchitecturesVision Encoder OptimizationVAE ArchitecturesTemporal Attention3D ConvolutionsCausal Video TransformersInference-Time Performance TradeoffsPerformance Bottleneck Identification
Soft Skills
Effective CommunicationCollaboration with ML ResearchersCollaboration with Creative TechnologistsCollaboration with Infrastructure Engineers
Tools & Technologies
NVIDIA InfrastructureTensorRTTriton Inference ServerNIM
Certifications & Qualifications
MS in Computer SciencePhD in Computer Science
Industry Keywords
AI/MLComputer VisionImage/Video GenerationMultimodal GenerationDeveloper Community Engagement
About the role
Key responsibilities & impact- Guide EMEA AI Native companies building image, video, and multimodal generation products in training and deploying pipelines on NVIDIA infrastructure
- Provide technical guidance on diffusion model architectures, including DiT, UNet, and flow matching, and their deployment across single- and multi-GPU environments
- Optimize generation pipelines
- Guide customers through the visual content generation stack, including codec-aware preprocessing, temporal consistency, video token representation, and efficient long-video inference
- Identify vision-workload performance bottlenecks, including memory-bound diffusion steps, attention scaling with resolution, and multi-GPU communication patterns for video
- Translate customer insights into actionable product feedback for NVIDIA research and engineering teams
- Contribute to the EMEA developer community through technical demos, workshops, and reference demos showcasing NVIDIA's stack
Requirements
What you’ll need- MS or PhD in Computer Science, Computer Vision, Machine Learning, or equivalent hands-on experience
- 5+ years in AI/ML with deep expertise in computer vision models
- Experience with diffusion model frameworks for image/video generation
- Understanding of vision encoder optimization, VAE architectures, and inference-time performance tradeoffs
- Strong communication skills, effective with ML researchers, creative technologists, and infrastructure engineers
- Familiarity with NVIDIA's inference stack: TensorRT, Triton Inference Server, and NIM
- Hands-on experience with video generation, including temporal attention, 3D convolutions, or causal video transformers
- Familiarity with codec-aware video pipelines and efficient video tokenization for generation at scale
- Published work or benchmarks in image/video generation, diffusion acceleration, or visual foundation models
Benefits
Comp & perks- Highly competitive salaries
- Comprehensive benefits package