NVIDIA
Full TimeDesign and deliver end-to-end AI solutions that unify NVIDIA's engineering data into production-ready systems for analytics, agents, and workflow automation. You'll own architecture through deployment, building data pipelines and copilots that enable production engineering teams to analyze ASIC systems at scale. The role requires 8+ years building production AI applications, strong Python skills, hands-on experience with LLMs and RAG, and expertise designing data pipelines for structured engineering data. Based onsite or remote depending on location.
Written from this posting by Neural Jobs AI. The full description is below.
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.
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NVIDIA builds the GPUs and the CUDA software stack that most modern AI is trained and served on, along with its own research in graphics, robotics and foundation models.
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