Responsibilities
- Design & Develop AI-Powered Implementation Agents - Architect and build AI agent workflows that autonomously generate ServiceNow configurations, implementation plans, user stories, and test scripts from customer requirements
- 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.)
- Design multi-step agentic processes that handle complex implementation logic—scope validation, dependency mapping, configuration generation, and quality assurance—with minimal human intervention
- Build and maintain prompt libraries, templates, and reusable patterns that encode delivery best practices and Gold Implementation standards into AI agent behavior, partnering with the platform administrator on library structure, versioning, and publication to end users
- Partner with AI Architects and Solution Architects to ensure agent outputs align with ServiceNow platform strategy, product direction, and enterprise readiness standards
- Lead Prompt Engineering & AI Quality Optimization - 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
- Design and execute systematic prompt evaluation frameworks that measure output accuracy, consistency, completeness, and adherence to ServiceNow implementation standards
- Continuously optimize prompt performance through A/B testing, output analysis, and iterative refinement—treating prompts as production code with version control and quality gates, incorporating user feedback and quality-audit signals surfaced by the platform administrator and the champions network
- Develop guardrails and validation logic that ensure AI-generated configurations meet enterprise quality standards before being deployed to the Expert Services community
- Stay current on evolving platform capabilities and techniques, rapidly incorporating new approaches that improve agent accuracy and efficiency
- Integrate AI Solutions with Professional Services GTM & Delivery - 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
- Design AI solutions that support accurate estimation by providing data on automated vs. manual effort, enabling services sales teams to right-size engagements
- Partner with delivery consultants to validate that AI-generated outputs are usable in real customer contexts, incorporating feedback into continuous improvement cycles
- Contribute to packaging AI-accelerated delivery into repeatable service offerings that can be positioned and sold by services and license sales teams
- Build Platform Integrations & Delivery Automation - Integrate AI agent outputs with the ServiceNow platform, customer engagement portals, and internal delivery systems to create seamless end-to-end automation
- Design APIs, data pipelines, and integration patterns that connect autonomous implementation tools with estimation systems, resource management platforms, and project tracking tools
- Build automated testing and validation workflows that verify AI-generated configurations against ServiceNow best practices and customer requirements before deployment
- Collaborate with the implementation platform team to embed AI capabilities into the customer and partner delivery experience
- Ensure all integrations meet enterprise security, data governance, and compliance standards
- Coordinate with the platform administrator on release management—staging validation, production rollout, and vendor escalation—so new AI capabilities reach users without disrupting platform stability
- Drive Measurement & Continuous Improvement - Establish quality metrics for AI agent outputs: accuracy rates, rework percentages, time savings, and customer acceptance rates, feeding these signals into the adoption and ROI metrics the platform administrator reports to A&M leadership
- Analyze agent performance data to identify improvement opportunities, failure patterns, and expansion use cases
- Contribute to the delivery acceleration roadmap by identifying where AI can fill capability gaps or replace manual processes
- Document engineering best practices, architectural patterns, and lessons learned to build organizational knowledge
- Participate in sprint ceremonies, code reviews, and cross-team collaboration in a two-week sprint cadence with monthly releases
Requirements
- deep expertise in prompt engineering, AI agent architecture, and an understanding of enterprise professional services delivery
- understanding of the professional services go-to-market motion—how services are scoped, sold, estimated, and delivered
- ability to balance technical depth in AI/LLM systems with practical business acumen
- ability to operate in a fast-paced sprint cadence where AI capabilities evolve rapidly, requiring continuous experimentation, rigorous quality validation, and close collaboration with solution architects, product managers, and delivery consultants across AMS, EMEA, and APAC+JPN
