NVIDIAPosted today

Senior Applied AI Engineer

Senior Applied AI Engineer at NVIDIA scores 86 out of 100 on AI centrality, which makes it a Level 4 role on this board.

Israel, YokneamseniorFull time

AI in this role

ragai-agents

NVIDIA has transformed accelerated computing through innovation powered by exceptional technology and people. Within ASIC networking product engineering group, you will help bring AI into product engineering by turning fragmented engineering data into scalable, production-ready solutions for analysis, decision-making, and efficiency.

In this role, you will define and deliver AI solutions that unify data across NVIDIA infrastructure and engineering systems, enabling advanced analytics for production engineering teams through AI agents, copilots, and workflow automation. You will own solutions end to end, from architecture and development through deployment, maintenance, and continuous improvement, and help shape how ASIC networking product engineering uses AI to scale engineering productivity.

What you'll be doing:

Design, build, and maintain AI solutions that improve our division efficiency across production, characterization, analysis, and operational workflows.
Develop agentic analytics capabilities that enable engineers to query, analyze, and reason over ASIC data using AI agents and copilots.
Consolidate data from multiple infrastructure and engineering systems into scalable, reliable pipelines and reusable services.
Partner with production engineering teams to identify pain points, define high-value use cases, and deliver measurable impact.
Build and support tools for data access, automation, reporting, anomaly detection, and engineering insight generation.
Collaborate across NVIDIA to align interfaces, improve data quality, and support scalable deployment models.
Drive continuous improvement through user feedback, monitoring, and roadmap planning.

What we need to see:

Bachelor’s in Computer Science, Software Engineering, Data Science, or a related field, or equivalent experience.
8+ years of experience as an AI solutions engineer, machine learning engineer, or software engineer building production AI/data solutions.
Strong experience designing, developing, deploying, and maintaining end-to-end AI applications in production.
Hands-on expertise with Python and modern software engineering practices.
Practical experience with LLMs, AI agents, RAG, workflow orchestration, and data/analytics applications.
Strong background building data pipelines, APIs, services, and applications on top of structured and semi-structured engineering data.
Strong communication skills and a proactive, ownership-driven mindset.
Advantage: experience in semiconductor, hardware, product engineering, test, characterization, or manufacturing analytics environments.

Ways to stand out from the crowd:

Experience building AI solutions for engineering or manufacturing organizations.
Familiarity with agent frameworks, vector databases, telemetry platforms, or internal knowledge/data systems.
Background in cross-functional work spanning software, data, infrastructure, and product engineering.
Proven track record of introducing new technical capabilities and driving adoption across engineering teams.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RagAI Agents

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Rag and AI Agents. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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