Role Description As a Sr. Delivery Acceleration AI Engineer, you will design, develop, and optimize AI-powered autonomous implementation solutions that transform how ServiceNow Expert Services deliver customer implementations. Working within the Delivery Acceleration team, you’ll focus on building and refining AI agents that automate ServiceNow configuration, generate implementation artifacts, and accelerate time-to-deploy—turning what previously required weeks of manual effort into AI-driven delivery that produces production-ready outputs in hours. This role requires deep expertise in prompt engineering, AI agent architecture, and an understanding of enterprise professional services delivery. You’ll work directly with autonomous implementation platforms built on large language models to create, test, and refine the AI-driven workflows that generate ServiceNow configurations, user stories, test scripts, and deployment packages for customer engagements. Critically, you must understand the professional services go-to-market motion—how services are scoped, sold, estimated, and delivered—because the AI solutions you build must align with how consultants and sales teams position and execute customer engagements. Your work directly impacts delivery speed, quality, margin, and the customer experience. Success requires balancing technical depth in AI/LLM systems with practical business acumen. You’ll 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. You will partner closely with the platform administrator who owns operations, adoption, and governance for the AI-powered deal and delivery document generator, ensuring the AI agents and prompt assets you build are deployed, scaled, and governed effectively across the global delivery community. What You Get to Do in This Role: - 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. Qualifications - 5+ years of experience in software engineering, AI/ML engineering, or technical consulting in enterprise software environments. - 2+ years of hands-on experience with large language models, prompt engineering, and AI agent development—not just using AI tools, but building production systems on top of them. - Demonstrated expertise in prompt engineering techniques: chain-of-thought reasoning, structured output generation, few-shot/zero-shot patterns, context window management, and systematic prompt evaluation. - Strong understanding of professional services delivery operations: how engagements are scoped, sold, estimated, staffed, and delivered. - Experience with API design, system integration, and building data pipelines that connect AI outputs to enterprise workflows. - Proficiency in Python, JavaScript/TypeScript, or similar languages used in AI agent development and automation. - Understanding of ServiceNow platform architecture, data models, and configuration patterns. - Excellent communication skills—ability to translate technical AI capabilities into business impact for non-technical stakeholders. - Experience working in agile, sprint-based environments with measurable delivery outcomes. Requirements - Not required but nice to have: - Experience building autonomous implementation or code generation systems that produce production-ready outputs. - Background in management consulting or professional services delivery operations. - Familiarity with ServiceNow implementation methodology and delivery best practices. - Experience with AI agent orchestration frameworks and multi-step workflow automation. - Knowledge of AI ethics, output validation, responsible AI practices, and enterprise governance requirements. - ServiceNow platform certifications (CSA, CIS, or similar). - Experience with enterprise estimation tools, resource management platforms, or delivery automation systems. Company Description It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.

• Work with LLMs, Knowledge Graphs, and Computer Vision to automate model generation • Train object detection models to extract shapes, lines, and symbols from P&IDs • Construct Knowledge Graphs and transform them into simulation-ready models • Design multi-agent systems with LangGraph using supervisor/worker patterns • Build Agentic RAG pipelines for generating simulation code • Own end-to-end solutions from concept to working prototype

• Own AI agent projects from zero to one: problem framing, design, implementation, deployment, monitoring, and iteration • Embed with business teams to map workflows (title production, underwriting, ops), identify high-leverage bottlenecks, and streamline them using AI • Design agent architectures (tools/functions, memory/state, retrieval, guardrails) and write prompts that are testable and versioned • Build full-stack features in C# and TypeScript/React that make agents usable, safe, and fast for internal users • Ship on Azure using Microsoft/Azure AI Foundry: model selection, evaluation, deployment pipelines, and production operations • Instrument quality: define success metrics, create evals/test suites, reduce hallucinations and failure modes, and drive reliability over time • Partner with security/compliance to ensure data handling and access patterns are appropriate for enterprise workflows.

**What you'll do** - Adding AI functionality into existing Node.js / Ruby / Python / React / React Native codebases - Building LLM-powered features: chat, summaries, classification, smart search, document Q&A - Designing lightweight RAG pipelines using embeddings and vector search - Working with vector DBs (pgvector, Pinecone, Qdrant) - Implementing safe, reliable LLM endpoints (OpenAI, Anthropic, Azure) - Working with PMs and clients to shape realistic AI features and transform workflows to reduce manual effort - Advising clients when NOT to use AI and considering trade-offs related to latency, accuracy, cost and maintainability