Senior Staff AI-Native Software Engineer – Agentic Systems

ServiceNow•Santa Clara, CA

4d•Hybrid

About The Position

AI Engineering and Delivery is the customer-obsessed engineering group building the agentic AI and enterprise-scale search systems that power Now Assist, AI Agents, and the AI-driven experiences our customers rely on every day. We build AI as foundational platform infrastructure — prioritizing robustness, performance, safety, and real-world customer impact at scale. You will design, build, and operate production-grade agentic AI systems embedded across ServiceNow's platform — autonomous agents that reason over real enterprise data, take action across workflows, and run safely at Fortune 500 scale.

Requirements

8+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems.
Hands-on depth designing, shipping, and operating agentic systems in production — multi-agent orchestration, tool calling, planning loops, memory, and failure recovery. Not prototypes.
Production-grade Python.
Working experience with frontier AI SDKs (Anthropic, Google, or OpenAI) — prompt engineering, structured outputs, and model evaluation in production settings.
Familiarity with RAG and retrieval patterns in production — vector stores, hybrid search, and retrieval evaluation metrics.
Track record of technical leadership: architecture ownership, code quality bar-raising, and mentoring engineers on production AI practices.

Nice To Haves

Deeper specialization in search and retrieval at scale or MLOps/model observability.
Published work or open-source contributions in agentic systems or retrieval.
Exposure to LLM fine-tuning or inference optimization in production.
Systems language (Go, Java, or C++) is a plus.

Responsibilities

Design and ship multi-agent systems — orchestration, tool use, planning loops, memory, and failure recovery — that operate reliably in production, not in notebooks.
Build agents that leverage ServiceNow's data layer — CMDB, Workflow Data Fabric, and Knowledge Graph — to make decisions with context no frontier model has on its own.
Own the guardrails: observability, human-in-the-loop controls, and compliance infrastructure that make autonomous systems safe to deploy at scale.
Work closely with our search team to ensure agents are grounded in accurate, low-latency retrieval — RAG pipelines, hybrid search, re-ranking, and evaluation — as a critical dependency of agentic quality.
Integrate frontier models (Anthropic, Google, OpenAI) into the Sense → Decide → Act → Govern architecture; evaluate trade-offs across cost, latency, and capability for production use cases.
Raise the technical bar through architecture decisions, code reviews, and coaching — particularly on agentic design patterns and production AI discipline.