Senior Workforce AI Solutions Engineer

Join Verisk's Workforce Technology Services organization as a Workforce AI Solutions Engineer. This role partners directly with business stakeholders across the enterprise to identify, design, build, and deploy AI-powered solutions using approved workforce AI platforms, including Microsoft Copilot, Copilot Studio, Claude, ChatGPT Enterprise, and associated technologies. The position serves as the technical lead and primary owner for a queue of business-submitted AI solution requests that require light-to-moderate implementation effort. The person in this role leads each engagement while remaining directly involved in solution design, development, testing, and delivery alongside a collaborating coworker. Requests may include workflow automation, AI agent development, system integrations, document intelligence, knowledge retrieval, process simplification, and productivity optimization. The role works with a partner team responsible for initial intake and triage so that simple questions, training needs, and requests requiring clarification are handled separately. This position receives qualified requests that need hands-on technical design and delivery but do not require a large-scale software development engagement. Purpose of the Role • Accelerate measurable business value from enterprise workforce AI investments. • Design and deliver AI-powered solutions that improve productivity, reduce manual effort, and simplify business processes. • Act as a technical advisor and solution architect for workforce AI technologies. • Establish reusable agents, skills, integrations, MCP components, and solution patterns that can be leveraged across the organization. • Drive responsible AI adoption while meeting enterprise security, governance, privacy, and compliance requirements. Key Responsibilities Request Queue Management and Solution Delivery • Lead and coordinate delivery activities with the coworker supporting each request, including work allocation, technical direction, issue resolution, and completion accountability. • Manage and prioritize a queue of qualified AI solution requests received from business units across the company. • Partner with the intake and triage function to confirm scope, readiness, ownership, dependencies, and the appropriate delivery path, then lead execution with the coworker assigned to support delivery. • Work directly with requesters to understand business processes, desired outcomes, data needs, risks, and success measures. • Translate business requirements into practical AI solutions using approved platforms and technologies. • Estimate effort, communicate delivery expectations, document decisions, and maintain visibility into request status and outcomes. • Provide appropriate implementation documentation, knowledge transfer, and transition to an ongoing support model. AI Solution Architecture and Engineering • Design AI-enabled workflows, agents, automations, and business process improvements. • Select the appropriate platform and implementation approach based on business needs, technical fit, security, maintainability, and cost. • Build solutions through simple builder experiences, low-code platforms, and pro-code development using Visual Studio or Visual Studio Code when needed. • Create reusable design patterns, templates, components, and engineering standards that improve delivery speed and consistency. • Evaluate scalability, observability, supportability, and lifecycle requirements before production use. Agent, Skill, and MCP Development • Build and maintain purpose-specific AI agents, actions, prompts, workflows, and orchestration logic. • Develop custom skills and Model Context Protocol (MCP) servers or integrations when platform-native capabilities are insufficient. • Extend AI platforms using APIs, SDKs, custom connectors, functions, and other approved integration methods. • Apply software development practices such as source control, testing, documentation, release management, and reusable code design. Integration, Data, and Authentication • Design secure connections between workforce AI platforms and enterprise data sources, knowledge repositories, business applications, and external functions. • Understand and apply modern authentication and authorization patterns, including OAuth, Microsoft Entra ID, application registrations, service principals, managed identities, delegated access, and API-based credentials. • Partner with application owners, data owners, security, privacy, architecture, and identity teams to establish approved access patterns. • Ensure solutions follow least-privilege principles and protect enterprise data throughout retrieval, processing, storage, and output. Platform Expertise and Continuous Improvement • Maintain strong working knowledge of Microsoft Copilot, Copilot Studio, Claude, ChatGPT Enterprise, related APIs, and emerging enterprise AI capabilities. • Understand the strengths, limitations, licensing considerations, and integration options of each platform. • Evaluate new capabilities and identify where they can improve solution quality, reduce development effort, or expand supported use cases. • Share lessons learned and reusable practices with AI enablement, platform, security, and engineering teams. Required Qualifications • Experience in solution architecture, software engineering, automation, cloud engineering, application integration, or a related technology discipline. • Demonstrated experience delivering business-facing technical solutions from requirements through implementation. • Hands-on experience with one or more enterprise AI platforms such as Microsoft Copilot, Copilot Studio, Claude, ChatGPT Enterprise, or comparable technologies. • Experience working with APIs, SDKs, integrations, automation technologies, and structured or unstructured enterprise data sources. • Working knowledge of enterprise identity, authentication, authorization, and secure integration patterns. • Ability to build thro