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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and optimizing AI applications, particularly in deep learning and generative AI, while leveraging GPU-accelerated architectures and collaborating effectively with diverse teams and partners.
Highest-signal resume keywords
Deep LearningMachine LearningPython ProgrammingGenerative AI ApplicationsMulti-Agent AI Systems
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Deep LearningMachine LearningPython ProgrammingC/C++ ProgrammingLinux EnvironmentsLLM DevelopmentGenerative AIAPI InteractionGPU-Accelerated ArchitecturesPrototyping AI Applications
Soft Skills
Interpersonal SkillsCollaboration
Tools & Technologies
PyTorchTensorFlowLangGraphLlamaIndexCrewAILangChainOpenAI Agents SDK
Certifications & Qualifications
BS/MS/PhD in Computer ScienceElectrical EngineeringAI/ML
Industry Keywords
AI InfrastructureEnterprise SystemsHigh-Performance Retrieval PipelinesAgentic SystemsDeveloper Enablement
Tech Stack
Tools & technologiesLinuxPythonPyTorchTensorflow
About the role
Key responsibilities & impact- Localize the future: Fine-tune LLMs to speak the authentic language of specific regions and industries.
- Develop and optimize training and inference workflows with partners and collaborate with internal NVIDIA development teams to improve our software stack.
- Build sophisticated agentic systems featuring multi-agent coordination, long-horizon reasoning, and sophisticated planning frameworks.
- Develop full-scale solutions, including domain-specific enterprise agents and high-performance retrieval pipelines (RAG) spanning various data sources.
- Optimize inference performance by bringing to bear GPU-accelerated frameworks and the full NVIDIA AI infrastructure stack.
- Build hands-on PoCs and reference architectures that serve as the blueprint for production-grade generative AI pipelines.
- Partner with high-growth startups and Enterprise ISVs to embed NVIDIA’s software stack into their core platforms, slashing the time to market for production-grade AI.
- Fuel partner innovation through hands-on developer enablement and thorough architectural reviews, turning sophisticated AI visions into production realities.
- Scale global expertise by crafting reusable assets and documentation that help field teams deploy agentic AI at scale.
Requirements
What you’ll need- BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience.
- 5+ years of experience in deep learning, machine learning, or distributed AI systems.
- Strong programming and debugging experience in Python, C/C++, and Linux environments.
- Background in using deep learning libraries like PyTorch or TensorFlow.
- Hands-on experience building LLM and generative AI applications.
- Experience working with agentic or multi-agent AI systems employing frameworks such as: LangGraph, LlamaIndex, CrewAI, LangChain, or OpenAI Agents SDK or similar orchestration frameworks.
- Experience building tool-using AI agents that interact with APIs, databases, and enterprise systems.
- Ability to rapidly prototype AI applications and build scalable GPU-accelerated architectures.
- Excellent interpersonal skills and the ability to collaborate with engineering teams, partners, and executive collaborators.
Benefits
Comp & perks- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development