Partner Alignment: Serve as the main technical contact for sophisticated AI collaborators, leading in-depth technical relationships and coordinating their roadmaps with NVIDIA's platform and emerging technology.
Architectural Leadership: Lead end-to-end prototyping engagements by defining requirements and crafting reference architectures that guide partners from initial concept to production-ready handoffs.
Applied AI Engineering: Build and architect enterprise-grade agentic AI systems, retrieval-augmented generation (RAG) pipelines, and multi-modal workflows, delivering high-performance, GPU-accelerated inference at scale.
Technical Problem Solving: Diagnose and resolve intricate system issues and performance bottlenecks across the full AI stack, from initial model selection to large-scale deployment.
Scalable Asset Creation: Produce reusable technical assets, including implementation guides and benchmarks, that accelerate time-to-value for both partners and their customers.
Multi-functional Collaboration: Work alongside NVIDIA’s internal engineering, research, and product teams to resolve critical partner challenges and speed up shared achievements.
Team Growth and Practice Building: Champion team guidelines for technical reviews and discovery, while mentoring new members and participating in hiring panels to expand the team’s depth.
Continuous Innovation: 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 sophisticated LLM workflows.
MS or advanced degree in Computer Science, AI, or a related field—or equivalent experience.
Deep proficiency in advanced GenAI techniques, including retrieval-augmented generation (RAG), prompt engineering, and production inference optimization.
Familiarity with agentic AI standards and expertise in building robust, production-grade multi-agent and multimodal architectures.
Hands-on experience with model fine-tuning (PEFT, LoRA), synthetic data generation, and building automated evaluation frameworks.
Strong command of GPU-optimized infrastructure, containerization, and MLOps pipelines for distributed training and inference.
Understanding of AI observability and responsible AI practices, including guardrail implementation and regulatory compliance like GDPR or HIPAA.
Experience bridging cloud and on-premises deployments and familiarity with Physical AI concepts such as edge inference.
Proven track record of communicating complex technical concepts to both developer teams and executive stakeholders.

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

AI engineeringmachine learningretrieval-augmented generationprompt engineeringproduction inference optimizationmodel fine-tuningsynthetic data generationautomated evaluation frameworksGPU optimizationMLOps

Soft Skills

technical problem solvingmulti-functional collaborationteam growthmentoringcommunication

Certifications

AI/ML certifications