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 through both low-code and pro-code approaches and to select the appropriate method for the request.
• Strong analytical, consulting, documentation, troubleshooting, and stakeholder communication skills.
• Ability to manage multiple requests, balance business urgency with technical risk, and drive work to completion.

Preferred Qualifications

• Experience building custom agents, AI assistants, agent actions, and multi-step AI workflows.
• Experience developing MCP servers, custom skills, plugins, connectors, or similar extensibility components.
• Experience with Microsoft Graph, REST APIs, Azure Functions, Logic Apps, Power Automate, GitHub, Visual Studio, or Visual Studio Code.
• Knowledge of retrieval-augmented generation, enterprise search, grounding, vector search, and knowledge-management patterns.
• Knowledge of responsible AI, data protection, security, compliance, auditability, and AI governance practices.
• Experience identifying and measuring productivity, process, service-quality, or business-value improvements.

Success Measures

• Timely movement of qualified requests from intake through delivery or an agreed disposition.
• Business requester satisfaction and adoption of delivered solutions.
• Demonstrated productivity, quality, service, or process improvements from implemented solutions.
• Creation and reuse of standard agents, skills, MCP components, connectors, and solution patterns.
• Reliable, secure, supportable solutions that comply with enterprise governance and architecture requirements.
• Clear documentation, ownership, and lifecycle management for solutions placed into use.

Candidate Profile

The successful candidate combines curiosity about emerging AI capabilities with disciplined enterprise engineering. They can move comfortably between a business process conversation and a technical design discussion, explain tradeoffs clearly, build practical solutions, and recognize when a request should be simplified, escalated, or redirected. They are accountable for outcomes, thoughtful about risk, and motivated by improving how employees work.

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