Core responsibilities include
Own the technical strategy and reference architecture for agentic AI across enterprise IT : agent runtimes, tool interfaces, memory and context management, orchestration, evaluation harnesses, and safety guardrails and establish them as the standard that partner engineering organizations build on.
Architect and personally build production agentic systems using LLM-based agents, tool calling, RAG, and orchestration frameworks, delivering AI-assisted operations across employee support, endpoint services, and IT support operations.
Design autonomous agents that execute complex, multi-step enterprise workflows end-to-end, coordinating approvals, vendor handoffs, cross-system data reconciliation, and exception handling with human-in-the-loop controls and own the reliability, observability, failure semantics, and blast-radius controls that make autonomy safe at enterprise scale.
Own the architecture of the integration and automation platform spanning ServiceNow, ERP and procurement systems, and endpoint-management platforms, including infrastructure, data pipelines, APIs, and user-facing applications and make the build/buy/deprecate calls that shape it for the next several years.
Define the engineering standards for how agentic systems are built, tested, evaluated, and operated: prompt and tool versioning, offline and online evals, regression suites, cost and latency budgets, security and data-governance patterns, and systematic failure-mode analysis.
Drive technical alignment across organizational boundaries, partner engineering teams, platform owners, security, and senior leadership resolving contested architectural decisions and building consensus without formal authority.
Multiply the organization. Mentor and grow senior and staff engineers through design partnership, code review, and technical coaching, and raise the engineering bar through the work you ship and the standards you enforce.
What We Need To See
Bachelor’s or Master’s degree in a related field, or equivalent experience. 12+ years of hands-on software engineering experience building and operating production systems at scale, with deep expertise in at least one of: Infrastructure, SRE, DevOps, Platform, or Production Engineering.
Demonstrated impact at Senior Staff scope architectural decisions that shaped systems across multiple organizations, and a track record of being the person others escalate to on the hardest technical problems.
Deep expertise designing and shipping production software systems integrations, automation platforms, APIs, and data pipelines for complex enterprise operations at scale, with ownership spanning design through long-term operation.
Proven experience taking agentic AI or LLM-based systems to production, including multi-step autonomous execution, tool calling, RAG, human-in-the-loop safeguards, exception handling, evaluation, and governance with measurable business outcomes.
Track record of modernizing enterprise IT operations platforms (e.g., asset management, endpoint services, IT supply chain, infrastructure operations), including migrating or replacing entrenched legacy systems.
Production-grade proficiency with infrastructure-as-code, CI/CD, containerization (Kubernetes), and cloud platforms (AWS, GCP, or Azure).
Experience with monitoring and observability tools (Prometheus, Grafana, Datadog, PagerDuty, or similar).
Fluent in Python, Go, or equivalent languages able to architect, write, and review production-quality code, not just scripts.
Demonstrated ability to grow other senior engineers and establish engineering practices that outlast your direct involvement.
Ability to drive technical direction across engineering, product, and senior leadership without formal authority, and to make complex trade-offs legible to VP and C-level audiences in business terms.
Ways To Stand Out From The Crowd
Experience defining an agentic AI or automation platform strategy for a large enterprise and driving its adoption across multiple engineering organizations.
Built agent orchestration frameworks, eval infrastructure, or LLM observability tooling as internal platforms used by other teams.
Background designing event-driven or workflow-orchestration architectures (e.g., Temporal, Airflow, Kafka-based systems) for long-running, stateful enterprise processes at scale.
Deep working knowledge of ServiceNow or comparable ITSM/ERP platforms at the API and data-model level, including extending them in ways their vendors didn’t anticipate.
Open-source contributions, patents, published work, or conference talks in distributed systems, automation, or applied AI.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative and autonomous, we want to hear from you!
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 200,000 USD – 322,000 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
