Senior Software AI Engineer, LLM Solutions

NVIDIA NVIDIA · Semiconductors · Yokneam, Israel

Senior Software AI Engineer to architect, develop, and support end-to-end software tools, focusing on integrating AI/ML solutions and agentic workflows into production systems for hardware platforms. Responsibilities include backend services, data pipelines, and user interfaces, with an emphasis on autonomous reasoning and action.

What you'd actually do

  1. 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.
  2. Design and implement agentic workflows—building multi-step, tool-using AI agents integrated into software platforms to enable autonomous reasoning and action.
  3. Build and maintain scalable data pipelines and ETL flows for logs and telemetry data to support intelligent automation and AI/ML workflows.

Skills

Required

  • 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.

Nice to have

  • 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)

What the JD emphasized

  • proven ability to seamlessly embed AI/ML solutions into production-ready tools
  • Design and implement agentic workflows
  • Strong Python skills and experience in integrating AI/ML solutions into production software environments.

Other signals

  • design and implement agentic workflows
  • integrate AI/ML solutions into production-ready tools
  • build and maintain scalable data pipelines