NVIDIA Developer Tools team is seeking a Senior Software Development Tech Lead to join our team in Shanghai. The team develops tools and services used by software developers worldwide. Our SDK Manager application enables software developers to retrieve the correct NVIDIA's SDKs, samples, and Tools, and set up the developer’s environment on the host machine and NVIDIA target devices with minimal effort.

We are seeking a senior individual contributor to spearhead AI‑powered experiences in our SDK delivery and developer workflows. You will be the technical lead for incorporating agentic solutions and related AI technologies into SDK Manager workflows used by developers worldwide.

What you will be doing:

Lead end‑to‑end development and implementation of features powered by AI that help developers discover, configure, install, and troubleshoot NVIDIA SDKs.
Architect and implement application flows that combine agents, LLMs, tools/APIs, and internal knowledge sources into reliable, goal-focused assistants.
Define technical direction, breakdown, and quality bar for AI features, and drive them from idea to production as a hands‑on IC.
Collaborate closely with product, UX, and partner engineering teams to translate vague AI use cases into concrete product experiences.
Establish guidelines for evaluation, observability, and continuous improvement of AI behaviors in production.

What we need to see:

Master's or Ph.D. in Computer Science, Engineering, or equivalent experience.
Over 8 years of direct software development experience, including substantial responsibility for complex, customer-facing systems or platforms. More than 3 years of practical experience in a technical AI position.
Proven track record as a technical lead or architect for significant features or products, with clear ownership of building and execution.
Proven experience developing and deploying LLM‑powered applications, with deep knowledge of diverse model architectures and capabilities.
Expertise in integrating LLMs with tools and data using APIs, agentic workflows and skills, retrieval‑augmented techniques, orchestration frameworks, and the Model Context Protocol (MCP).
Strong systems thinking across latency, reliability, security, privacy, and cost optimization when embedding AI into real‑world workflows.
Strong hands‑on coding skills in at least one of: Python, Node.js/TypeScript or Golang, plus comfort working in Linux environments.
Real daily use of AI coding tools
Excellent communication skills and ability to influence technical direction across teams while remaining as an individual contributor.
Ability to multitask effectively in a dynamic environment.

Ways to stand out from the crowd:

Experience in deploying LLM models in cloud environments (e.g., AWS, Azure, GCP) and on-premises infrastructure.
Hands-on experience building and operating agentic workflows for production applications using frameworks such as LangChain and platforms like OpenClaw and OpenShell.
Experience building developer‑facing tools, SDK workflows, or infrastructure used by engineers.
Contributions to internal or open‑source AI frameworks, tooling, or evaluation methodologies.
Demonstrated history of mentoring senior engineers and elevating engineering standards around building, code quality, and reliability.

****What we need to see: ****

Master's or Ph.D. in Computer Science, Engineering, or equivalent experience.

Over 8 years of direct software development experience, including substantial responsibility for complex, customer-facing systems or platforms. More than 3 years of practical experience in a technical AI position.

Proven track record as a technical lead or architect for significant features or products, with clear ownership of building and execution.

Proven experience developing and deploying LLM‑powered applications, with deep knowledge of diverse model architectures and capabilities.

Expertise in integrating LLMs with tools and data using APIs, agentic workflows and skills, retrieval‑augmented techniques, orchestration frameworks, and the Model Context Protocol (MCP).

Strong systems thinking across latency, reliability, security, privacy, and cost optimization when embedding AI into real‑world workflows.

Strong hands‑on coding skills in at least one of: Python, Node.js/TypeScript or Golang, plus comfort working in Linux environments.

Real daily use of AI coding tools

Excellent communication skills and ability to influence technical direction across teams while remaining as an individual contributor.

Ability to multitask effectively in a dynamic environment.

Ways to stand out from the crowd:

Experience in deploying LLM models in cloud environments (e.g., AWS, Azure, GCP) and on-premises infrastructure.

Hands-on experience building and operating agentic workflows for production applications using frameworks such as LangChain and platforms like OpenClaw and OpenShell.

Experience building developer‑facing tools, SDK workflows, or infrastructure used by engineers.

Contributions to internal or open‑source AI frameworks, tooling, or evaluation methodologies.

Demonstrated history of mentoring senior engineers and elevating engineering standards around building, code quality, and reliability.