Solution Architect - AI & Data

Solution Architect - AI & Data, Expert Services

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 Service Now 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 operationalizing 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…