Solution Architect

Who you are

• 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
• Experience facilitating executive AI strategy engagements — including visioning workshops, maturity assessments, and transformation roadmap sessions — where the output is a customer-owned AI narrative, not just a delivery plan
• 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
• AI domain knowledge required, including: Generative AI Skills, AI Agents and Agentic workflows, Retrieval-Augmented Generation (RAG), Knowledge Graphs, Agent-to-Agent (A2A), and Model Context Protocol (MCP)
• 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 the job involves

• As part of the Expert Services AI Practice, the Solution Architect - AI & Data will serve as a senior strategic and technical lead, reshaping how enterprise organizations adopt AI at the core of their operating models
• This role goes beyond implementation — it bridges C-suite advisory, enterprise architecture, and organizational change to deliver lasting transformation outcomes
• The Solution Architect - AI & Data will operate at the intersection of AI strategy, solution architecture, and customer success — leading engagements from transformation vision and use-case definition through architecture design, governance, adoption, and ongoing value realization
• 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

Benefits

• Generous family leave
• Matched donations
• Annual learning stipends
• Flexible PTO
• Competitive retirement plan
• Paid volunteer time