Senior Software AI Engineer, LLM Solutions
Indexed description
The team develops and sustains a range of capabilities for NVIDIA's next-generation hardware platforms—bridging traditional software engineering with with AI-based automation and intelligent debugging solutions. Your role will be to ensure these advanced technologies are delivered through a cohesive system architecture and a superior user experience, accelerating issue resolution and improving product reliability for the world's most advanced GPU and data center technologies
What You'll Be Doing
- Architect, develop, and support end-to-end software tools across the full engineering stack—from robust backend services and data processing to intuitive, user-centric interfaces.
- Design and implement agentic workflows—building multi-step, tool-using AI agents integrated into software platforms to enable autonomous reasoning and action.
- Build and maintain scalable data pipelines and ETL flows for logs and telemetry data to support intelligent automation and AI/ML workflows.
- B.Sc. or M.Sc. in Computer Science, Electrical/Computer Engineering, or equivalent practical experience.
- 5+ years of experience in software engineering, with a proven track record of developing complex Client-Server applications.
- Strong proficiency in Web technologies (e.g., React, Angular, Vue, or similar) and a solid UX/UI mindset for building intuitive, interactive user interfaces.
- Strong Python skills and experience in integrating AI/ML solutions into production software environments.
- Solid understanding of software architecture and system-level design (APIs, services, and data flow).
- Hands-on experience with Linux, Git, and containerization (e.g., Docker, Kubernetes).
- Strong analytical and problem-solving skills, with an eagerness to learn and optimize complex software systems.
- Experience with full-stack or frontend web development, particularly building internal tools or dashboards using React
- Hands-on experience designing and implementing agentic workflows (e.g., LangChain, LangGraph, AutoGen, or similar frameworks)
- Experience with LLM fine-tuning, prompt engineering, or RAG pipelines
- Familiarity with hardware debugging, observability/logging systems, or chip/system reliability analysis
- Experience with vector databases (FAISS, Pinecone, Milvus) or MLOps tools (MLflow, Kubeflow)
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