Function as the chief technical representative for advanced AI partners, handling detailed technical interactions and synchronizing their roadmaps with NVIDIA's platform and emerging technology
Lead end-to-end prototyping engagements by defining requirements and crafting reference architectures that guide partners from initial concept to production-ready handoffs
Architect and build enterprise-grade agentic AI systems, RAG pipelines, and multi-modal workflows, ensuring high-performance, GPU-accelerated inference at scale
Diagnose and resolve intricate system issues and performance bottlenecks across the full AI stack, from initial model selection to large-scale deployment
Produce reusable technical assets, including implementation guides and benchmarks, that accelerate time-to-value for both partners and their customers
Collaborate with NVIDIA’s internal engineering, research, and product teams to resolve critical partner issues and speed up shared success
Champion team guidelines for technical reviews and discovery, while mentoring new members and participating in hiring panels to expand the team’s depth
Commit to continuous professional growth through formal AI/ML certifications and special projects that elevate your value within the NVIDIA ecosystem

Requirements

8+ years of relevant engineering experience crafting, developing, and deploying AI/ML systems and complex LLM-powered workflows
Strong proficiency in advanced GenAI and LLM approaches, including proprietary vs. open model selection, retrieval-augmented generation (RAG), prompt engineering, and production inference optimization
Familiarity with agentic AI standards (LangGraph, LangChain, MCP) and expertise in multimodal AI systems including vision-language models and audio/video workflows
Hands-on experience with fine-tuning (PEFT, LoRA), synthetic data generation, and implementing automated AI evaluation frameworks to measure quality and safety
Strong command of GPU-optimized infrastructure (Docker, Kubernetes), microservices, and MLOps pipelines for automated training and distributed inference
Experience with AI observability (trace logging, latency monitoring) and a solid understanding of responsible AI, including guardrails, data governance, and regulatory considerations (HIPAA, GDPR)
Experience connecting cloud and on-premises deployments, hybrid architectures, and familiarity with Physical AI concepts such as simulation environments and edge inference
Proven ability to communicate complex technical concepts to diverse audiences and experience in technical engagement roles involving discovery and requirements gathering
MS or advanced degree in Computer Science, AI, or a related field—or equivalent experience.

Benefits

equity
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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI systemsML systemsGenAILLMRAGprompt engineeringGPU-optimized infrastructureMLOpsfine-tuningsynthetic data generation

Soft Skills

communicationmentoringcollaborationproblem-solvingtechnical engagement

Certifications

AI/ML certifications