About the job
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.
Responsibilities
Design and ship multi-agent systems — orchestration, tool use, planning loops, memory, and failure recovery — that operate reliably in production.
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.
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.
Qualifications
Minimum
8+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems.
Formal grounding in machine learning fundamentals — modeling, training, evaluation, and the principles behind modern deep learning, LLMs, and agent architectures.
Hands-on depth designing, shipping, and operating agentic systems in production — multi-agent orchestration, tool calling, planning loops, memory, and failure recovery.
Proven experience building and operating production-grade, full-stack AI systems and services end to end — model integration, APIs, serving infrastructure, and the application layer.
Production-grade Python.
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.
Preferred
Specialization in search and retrieval at scale — RAG pipelines, hybrid search, vector stores, re-ranking, and retrieval evaluation — 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.