Solution Architect - AI & Data

• Lead enterprise AI transformation engagements - from opportunity identification and business case development through to operating model design and value realization.
• Advise C-suite and senior stakeholders on AI strategy prioritization frameworks and transformation roadmaps tailored to their industry maturity and risk appetite.
• Facilitate discovery workshops current-state assessments and future-state visioning sessions to establish a shared transformation agenda.
• Define AI-enabled target operating models including process redesign workforce impact analysis and governance structures.

• Design end-to-end solution architectures spanning Now Assist AI Agents Agentic workflows AI Control Tower RAG knowledge graphs and enterprise integrations (A2A MCP).
• Lead scoping and solutioning for complex multi-workload AI engagements - ensuring architectural integrity scalability and alignment to customer outcomes.
• Provide hands-on architecture leadership during pilot and early-phase delivery establishing patterns and standards for broader team execution.
• Develop reusable practice IP: reference architectures deployment patterns transformation playbooks and verticalized use-case catalogs.
• Architect enterprise data catalog strategies using platforms defining target-state designs that align metadata management data lineage and governance structures to broader AI and business objectives.
• Define integration patterns and architectural standards for connecting data catalog solutions across heterogeneous enterprise environments - cloud platforms data warehouses BI layers and ServiceNow workflows.

• Lead the architectural design of enterprise data catalog programs - defining scope platform selection criteria governance operating models and phased adoption roadmaps.
• Advise on the strategic application of knowledge graph concepts semantic technologies and ontological frameworks (RDF SPARQL) to enterprise data and AI use cases.
• Shape data architecture principles and standards that underpin AI readiness - including data lineage metadata quality classification taxonomies and access governance.
• Translate complex data architecture requirements into clear actionable designs that can be executed by delivery and technical teams.
• Define success metrics and maturity benchmarks for data catalog programs enabling customers to track progress and demonstrate value to executive stakeholders.

• Define and embed AI governance frameworks covering data stewardship model risk bias controls audit trails and compliance postures.
• Support customers in operationalize responsible AI practices aligned to regulatory requirements and internal policies.
• Establish data governance frameworks that position metadata management data stewardship and knowledge graph capabilities as foundational trust layers for enterprise AI programs.
• Guide customers in regulated industries on aligning data catalog and governance architectures to compliance and regulatory obligations embedding controls into the design rather than as an afterthought.
• Partner with AI Control Tower to establish monitoring observability and continuous optimization capabilities post-deployment.

• Lead AI adoption strategies including readiness assessments stakeholder engagement plans AI literacy programmers and change communications.
• Define adoption KPIs and value realization metrics; track and report outcomes; provide consultative guidance for continuous optimization and expansion.
• Coach customer teams to build internal AI capability reducing dependency and accelerating long-term self-sufficiency.
• Monitor adoption usage and value realization metrics post-deployment; provide recommendations for risk mitigation and growth.

• Collaborate cross-functionally with Sales Solution Consulting Customer Success Platform and Product teams to embed AI advisory across the customer lifecycle.
• Build and maintain industry-specific AI advisory playbooks and frameworks - verticalized use-case catalogs value models governance templates and deployment patterns - to support scalable repeatable delivery.
• Act as a thought-leader internally and externally: contribute to white papers points-of-view reference architectures best-practice guides and represent the organization at AI forums and customer briefings.
• Support pre-sales by qualifying opportunities shaping proposals and presenting transformation vision to executive buyers.

• Experience in leveraging or critically thinking about how to integrate AI into work processes decision-making or problem-solving. This may include using AI-powered tools automating workflows analyzing AI-driven insights or exploring AI's potential impact on the function or industry.
• 10+ years of experience in management consulting enterprise architecture or a senior technology advisory role with a demonstrated focus on Artificial Intelligence Machine Learning or digital transformation at enterprise scale.
• ServiceNow domain knowledge including: Now Assist and Generative AI Skills / Skill Kit AI Agents and Agentic workflows AI Control Tower Retrieval-Augmented Generation (RAG) Knowledge Graphs Agent-to-Agent (A2A) and Model Context Protocol (MCP).
• Proven track record designing and managing complex multi-stakeholder AI or digital-transformation engagements - including use-case definition business case development integration data strategy governance and operational adoption.
• Strong understanding of enterprise data architecture data quality knowledge management integrations and compliance and regulatory frameworks.
• Demonstrated ability to architect enterprise data catalog and metadata management strategies with knowledge of platforms.
• Excellent communication and interpersonal skills - ability to articulate technical and business value to C-level executives align stakeholders and influence strategic decision-making.
• Experience working in fast-paced dynamic environments with capability to manage ambiguity and tailor consulting deliverables to different customer maturity levels from early adopters to AI-ready enterprises.
• Working knowledge of knowledge graph principles semantic technologies and standards (RDF SPARQL) as applied to enterprise data architecture.
• Broad familiarity with the ServiceNow platform and modules (ITSM CSM FSM HRSD App Engine) ideally including implementation or architecture experience. ServiceNow certifications (Certified System Administrator Certified Implementation Specialist Certified Technical Architect) are desirable.
• AI/ML certifications (e.g. AWS Machine Learning Specialty Google Professional Machine Learning Engineer or equivalent) are desirable.
• Background in one or more target industries - Financial Services Healthcare Public Sector Manufacturing or Retail - is highly desirable.

What We Do

As the AI platform for business transformation we're putting AI to work across organizations — freeing people for work that matters. Making old tech work with new tech. Reaching across departments from the front office to the back office and every office in between. Our ambition? To become the AI defining enterprise software company of the 21st century (or "AI DESCO21C" as we like to call it). With more than 8100+ customers we serve approximately 90% of the Fortune 500® and we're proud to be a Fortune 100 Best Companies to Work For® and World's Most Admired Companies™.Explore your future career with us visit www.servicenow.com/careers.From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

Why Work With Us

By joining ServiceNow you are part of an ambitious team of change-makers who have a restless curiosity and a drive for ingenuity. We're committed to helping our people do their best work and live their best lives so we can fulfill our purpose together. At the fastest-growing enterprise software company you can grow your career faster.

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