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Senior Staff AI Engineer (Veza)
ServiceNow
5 hours ago
Full-time
On-site
Santa Clara, California, United States
The Access AI team is the customer-obsessed engineering group building the agentic AI and enterprise-scale harness platform that powers agents for Access Management across all Identity Security Products. We build AI as foundational platform infrastructure — prioritizing robustness, performance, safety, and real-world customer impact at scale
You will architect, build, and operate production-grade agentic AI systems—autonomous agents that reason over enterprise data and execute mission-critical identity security actions at Fortune 500 scale. As a tier-one technical leader at the intersection of Agent research and large-scale backend systems, you will shape the architectural patterns, scalability guardrails, and strategic vision for autonomous enterprise security
Your core focus areas:
Agentic architecture. Design and ship multi-agent systems — orchestration, tool use, planning loops, memory, and failure recovery — that operate reliably in production, not in notebooks Enterprise-grounded reasoning. Build agents that leverage Access Graph, Access Reviews, and permission and risk data — to make decisions with context no frontier model has on its own Trust, safety, and governance. Own the guardrails: observability, human-in-the-loop controls, and compliance infrastructure that make autonomous systems safe to deploy at scale Retrieval and grounding. Work closely with our different product teams, platform and graph teams to ensure agents are grounded in accurate, low-latency retrieval — RAG pipelines, semantic search, re-ranking, and evaluation — as a critical dependency of agentic quality Model integration and evaluation. Integrate frontier models, evaluate trade-offs across cost, latency, and capability for production use cases Engineering leadership. Raise the technical bar through architecture decisions, code reviews, and coaching — particularly on agentic design patterns and production AI discipline Benefits
Generous family leave Matched donations Annual learning stipends Flexible PTO Competitive retirement plan Paid volunteer time 8+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems Familiarity with RAG and retrieval patterns in production — vector stores, hybrid search, and retrieval evaluation metrics Production-grade Python. Systems language (Go, Java, or C++) is a plus Working experience with frontier AI SDKs (Anthropic, Google, or OpenAI) — prompt engineering, structured outputs, and model evaluation in production settings Track record of technical leadership: architecture ownership, code quality bar-raising, and mentoring engineers on production AI practices Hands-on depth designing, shipping, and operating agentic systems in production — multi-agent orchestration, tool calling, planning loops, memory, and failure recovery. Not prototypes Deeper specialization in search and retrieval at scale or MLOps/model observability Exposure to LLM fine-tuning or inference optimization in production Published work or open-source contributions in agentic systems or retrieval
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Agentic architecture. Design and ship multi-agent systems — orchestration, tool use, planning loops, memory, and failure recovery — that operate reliably in production, not in notebooks Enterprise-grounded reasoning. Build agents that leverage Access Graph, Access Reviews, and permission and risk data — to make decisions with context no frontier model has on its own Trust, safety, and governance. Own the guardrails: observability, human-in-the-loop controls, and compliance infrastructure that make autonomous systems safe to deploy at scale Retrieval and grounding. Work closely with our different product teams, platform and graph teams to ensure agents are grounded in accurate, low-latency retrieval — RAG pipelines, semantic search, re-ranking, and evaluation — as a critical dependency of agentic quality Model integration and evaluation. Integrate frontier models, evaluate trade-offs across cost, latency, and capability for production use cases Engineering leadership. Raise the technical bar through architecture decisions, code reviews, and coaching — particularly on agentic design patterns and production AI discipline Benefits
Generous family leave Matched donations Annual learning stipends Flexible PTO Competitive retirement plan Paid volunteer time 8+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems Familiarity with RAG and retrieval patterns in production — vector stores, hybrid search, and retrieval evaluation metrics Production-grade Python. Systems language (Go, Java, or C++) is a plus Working experience with frontier AI SDKs (Anthropic, Google, or OpenAI) — prompt engineering, structured outputs, and model evaluation in production settings Track record of technical leadership: architecture ownership, code quality bar-raising, and mentoring engineers on production AI practices Hands-on depth designing, shipping, and operating agentic systems in production — multi-agent orchestration, tool calling, planning loops, memory, and failure recovery. Not prototypes Deeper specialization in search and retrieval at scale or MLOps/model observability Exposure to LLM fine-tuning or inference optimization in production Published work or open-source contributions in agentic systems or retrieval
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