ServiceNow AI Engineer

ServiceNow AI Engineers - Minneapolis , MN - Onsite

ServiceNow AI Engineers are part of the company's AI, Engineering, & Product teams, which develop the ServiceNow AI Platform and build intelligent, scalable AI solutions for enterprise customers ServiceNow. These roles span multiple specializations, from Forward Deployed Engineering to Agentic Systems and Conversational & Agentic AI.

Core Responsibilities

Build and maintain AI platform infrastructure that enables customers to automate workflows, scale operations, and transform how they get work done ServiceNow.

Design and develop AI agents that reason over enterprise data, execute multi-step workflows, and operate safely at Fortune 500 scale

Orchestrate multi-agent systems with tool use, planning loops, memory management, and failure recovery ServiceNow.

Implement Retrieval-Augmented Generation (RAG), hybrid search, and grounding pipelines to ensure agents are accurate and context-aware ServiceNow.

Integrate frontier AI models (e.g., Anthropic, Google, OpenAI) into production systems, evaluating trade-offs for cost, latency, and capability ServiceNow.

Ensure reliability, observability, and governance - including fault tolerance, latency monitoring, cost attribution, and compliance

Develop prompt management systems for stable agent behavior across model updates .

Collaborate with product, engineering, and customer teams to co-innovate in real time, proving AI capabilities in production ServiceNow.

Specialized Role Examples

Staff AI Engineer Conversational & Agentic AI

Own the agent harness - orchestration layer for routing inputs, managing context, invoking tools, retries, and execution state.

Design for enterprise-scale reliability and observability.

Build prompt infrastructure for versioning, templating, and evaluation.

Create evaluation frameworks for agent behavior.

Senior Staff AI-Native Software Engineer Agentic Systems ServiceNow

Design enterprise-grounded reasoning using ServiceNow's CMDB, Workflow Data Fabric, and Knowledge Graph.

Build trust, safety, and governance systems for autonomous AI.

Lead engineering decisions on agentic design patterns and production AI discipline.

Skills & Qualifications (Typical)

Strong programming skills in Python, Java, or other relevant languages.

Experience with machine learning, LLMs, MLOps tools (e.g., MLflow, Docker) Indeed.

Knowledge of RAG, vector databases, and model integration.

Familiarity with cloud infrastructure and observability tools.

Experience in enterprise-scale AI systems and agentic workflows.