About the job
NVIDIA is seeking an outstanding Applied AI Builder, Enterprise Agent Systems to join our team building production-grade AI applications for the enterprise. In this role, you will lead by example as both a hands-on developer and technical expert, building proof-of-concept solutions, reference architectures, and deployable single-agent and multi-agent systems that solve real business problems.
Responsibilities
Build applied AI applications and agentic systems that solve real enterprise problems across functions and industries
Design single-agent and multi-agent workflows for tool use, retrieval, memory, planning, handoffs, and human-in-the-loop execution.
Build full-stack systems that move from prototype to secure production deployment, including APIs, orchestration, observability, evaluation, identity, and rollback.
Integrate with enterprise systems such as document stores, internal tools, codebases, data platforms, workflow engines, and business applications.
Use coding agents such as Codex, Claude Code, Open/NemoClaw, or similar OSS tools as part of implementation, testing, debugging, refactoring, and release workflows to scaling partners.
Define evals and feedback loops—including synthetic data generation and synthetic evaluation where useful—for task completion, reliability, latency, safety, cost, and measurable business impact.
Qualifications
Minimum
MS or PhD degree in Computer Science/Engineering, Machine Learning, Data Science, Electrical Engineering or a closely related field.
5+ years of relevant work experience in developing and deploying AI models at scale as a Software Engineer or Deep Learning engineer or Solutions Architect
Evidence that you have built and shipped applied AI applications, enterprise copilots, agentic workflows, or automation systems that people actually use.
Strong understanding of foundation model behavior in real systems, including prompting, context engineering, retrieval, tool use, fine-tuning tradeoffs, and evaluation.
Real experience with multi-agent workflows, orchestration patterns, or complex long-running task systems.
Strong programming skills in Python plus at least one of TypeScript, Go, Rust, or C++.
Experience with synthetic data generation and evaluation, including synthetic tasks, traces, or test corpora used to improve coverage, quality, or robustness.
Familiarity with GPU-backed inference systems, performance tradeoffs, and cost-quality tradeoffs.
High agency, strong ownership, and a bias toward shipping.
Preferred
Meaningful OSS contributions in agents, enterprise integrations, evals, observability, or developer tooling.
Experience deploying AI systems into enterprise, security-conscious, or regulated environments.
Experience with secure execution, sandboxing, permissioned tool use, secrets handling, or auditability.
Strong examples of multi-agent coordination in production.
Familiarity with MCP, A2A-style communication patterns, or advanced agent interoperability.