Senior Socd Applied AI Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Applied AI Engineer at NVIDIA focused on developing LLM-powered tools, agents, and automation solutions to streamline SOC integration workflows. The role involves building RAG systems, AI-assisted coding workflows, and owning the reliability and evaluation of these AI systems. Requires strong Python skills, experience shipping AI applications to production, and hands-on experience with LLM frameworks and agentic workflows.

What you'd actually do

  1. Develop LLM-powered tools for high-value execution tasks: design review summarization, signoff status aggregation, integration checklist enforcement, CI/CD pipeline gating, and cross-team status reporting.
  2. Build and deploy RAG-based knowledge systems grounded in internal design documentation and execution artifacts.
  3. Design AI-assisted coding workflows, including agent-based development tools, reusable prompt templates, and structured skills to accelerate engineering productivity.
  4. Own reliability and evaluation of AI systems, including logging, tracing, prompt regression testing, and output validation frameworks.
  5. Collaborate closely with SOCD execution and methodology teams to scope problems, validate solutions, and define metrics for productivity gains from deployed automation.

Skills

Required

  • BS/MS in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience)
  • 6+ years of experience building production-grade software systems
  • Proven experience shipping AI/LLM-powered applications, agents, or automation workflows into production environments
  • Strong Python skills
  • Practical experience building LLM-powered agents or agentic workflows
  • Hands-on experience with LLM application frameworks (LangChain, LlamaIndex, or equivalent) and RAG architectures
  • Solid software engineering fundamentals and production mindset
  • Ability to identify repetitive, high-friction, or knowledge-intensive workflows and turn them into practical AI-enabled tools, automations, or assistants
  • Demonstrated end-to-end ownership of engineering solutions
  • Excellent communication skills and a collaborative, proactive approach

Nice to have

  • Advanced AI techniques: fine-tuning or domain-specific prompt engineering
  • experience with MCP (Model Context Protocol) or similar tool-calling standards for interoperable agent ecosystems
  • multi-agent orchestration frameworks
  • Knowledge of ASIC development and SOC integration
  • Experience building lightweight internal tools or full-stack applications

What the JD emphasized

  • Proven experience shipping AI/LLM-powered applications, agents, or automation workflows into production environments
  • Solid software engineering fundamentals and production mindset
  • Demonstrated end-to-end ownership of engineering solutions, from architecture and development to deployment, integration, and ongoing operations/support

Other signals

  • Develop LLM-powered tools for high-value execution tasks
  • Build and deploy RAG-based knowledge systems
  • Design AI-assisted coding workflows, including agent-based development tools
  • Own reliability and evaluation of AI systems
  • Proven experience shipping AI/LLM-powered applications, agents, or automation workflows into production environments