Sr. Delivery Acceleration AI Engineer

ServiceNow ServiceNow · Enterprise · Atlanta, GA +1 · Customer Outcomes

This role focuses on designing, developing, and optimizing AI-powered autonomous implementation solutions using large language models (LLMs) to automate ServiceNow configurations, generate implementation artifacts, and accelerate time-to-deploy. The engineer will build and refine AI agents, lead prompt engineering efforts, and integrate these solutions with professional services go-to-market and delivery workflows.

What you'd actually do

  1. Architect and build AI agent workflows that autonomously generate ServiceNow configurations, implementation plans, user stories, and test scripts from customer requirements
  2. Develop, test, and iterate sophisticated prompt chains and orchestration patterns that produce consistent, production-quality outputs across ServiceNow product workflows (ITSM, CSM, HRSD, SPM, etc.)
  3. Design multi-step agentic processes that handle complex implementation logic—scope validation, dependency mapping, configuration generation, and quality assurance—with minimal human intervention
  4. Serve as a subject matter expert in prompt engineering techniques for large language models, including chain-of-thought reasoning, few-shot learning, structured output generation, and context window optimization
  5. Understand the full professional services lifecycle—from pre-sales scoping and estimation through delivery execution and go-live—and ensure AI agent capabilities map to real engagement workflows

Skills

Required

  • Prompt engineering
  • AI agent architecture
  • Large language models (LLMs)
  • Orchestration patterns
  • Chain-of-thought reasoning
  • Few-shot learning
  • Structured output generation
  • Context window optimization
  • Systematic prompt evaluation frameworks
  • A/B testing
  • Version control
  • Professional services lifecycle understanding
  • Integration with enterprise systems

Nice to have

  • Autonomous implementation solutions
  • ServiceNow configuration
  • Implementation plans
  • User stories
  • Test scripts
  • Scope validation
  • Dependency mapping
  • Quality assurance
  • Prompt libraries
  • Templates
  • Reusable patterns
  • AI Architects
  • Solution Architects
  • ServiceNow platform strategy
  • Product direction
  • Enterprise readiness standards
  • Output accuracy
  • Consistency
  • Completeness
  • Adherence to implementation standards
  • User feedback
  • Quality-audit signals
  • Guardrails
  • Validation logic
  • Pre-sales scoping
  • Estimation
  • Delivery execution
  • Go-live
  • ServiceNow product workflows (ITSM, CSM, HRSD, SPM)
  • APIs
  • Data pipelines
  • Integration patterns
  • Estimation systems
  • Resource management platforms
  • Project tracking tools
  • Automated testing

What the JD emphasized

  • Critically, you must understand the professional services go-to-market motion
  • Treating prompts as production code with version control and quality gates

Other signals

  • AI agents
  • LLMs
  • prompt engineering
  • automation
  • enterprise solutions