Lead Architect, Application & Product- AI

Wolters Kluwer•Coppell, TX

About The Position

As Lead Application & Product Architect, you will play a central technical role in shaping new cloud, AI, and agentic products and the broader platform. You will own architecture direction and technical guardrails across application, data, integration, cloud, and AI capabilities, combining delivery velocity, AI-native development, and technical excellence. You will establish production-grade architecture patterns that engineering teams can execute and evolve through Spec-Driven Development and Architecture-as-Code practices, while ensuring decisions meet Wolters Kluwer requirements for security, data protection, risk, compliance, and governance. The role partners with Product and Engineering and relevant control and governance functions to shape architecture, review designs and security, and establish reusable patterns for cloud-first, API-first, and agentic-first solutions. It includes defining production-ready AI and agentic capabilities, integration and data patterns, evaluation and validation approaches, and actionable, version-controlled architecture artifacts that evolve alongside delivery.

Requirements

Bachelor’s degree in computer science, Software Engineering, Information Systems, or a related field, or equivalent practical experience.
10+ years of software engineering experience, including significant application or product architecture responsibility for complex commercial software.
Hands-on experience architecting AI/ML, generative AI, or agentic capabilities within customer-facing production software.
Practical experience with Agentic Product Development Lifecycle and Spec-Driven Development, including architecture specifications, evaluation strategies, and traceability.
Experience designing secure AI-enabled systems, including data protection, identity, access controls, threat modeling, and AI-specific security risks.
Strong expertise in cloud-native architectures, APIs, microservices, distributed systems, and integration architectures.
Experience with modern technology stacks, CI/CD, DevSecOps, and automated quality gates.
Experience quickly understanding existing enterprise and legacy architectures and evolving them incrementally.
Knowledge of Enterprise Architecture, Security, Risk, Compliance, and Architecture Governance, with the ability to incorporate these requirements into technical solutions early in the lifecycle.
Strong systems-thinking skills across application, data, integration, cloud, AI and agentic, security, and operational architecture, and ability to translate complex systems into clear boundaries and patterns.
Architectural judgment to evaluate trade-offs across scalability, resiliency, security, performance, cost, maintainability, delivery speed, and technical debt.
Strong understanding of production AI and agentic architecture, including context engineering, evaluation, orchestration, observability, traceability, model and agent integration, security, and token and cost optimization.
Strong understanding of modern data architecture, including data modeling, lineage, data quality, governance, scalable access patterns, and batch, real-time, and event-driven integration.
Ability to apply Architecture-as-Code and Spec-Driven Development by creating version-controlled, traceable architecture artifacts that evolve with implementation.
Strong understanding of production architecture, including observability, telemetry, reliability, resiliency, performance, deployment architecture, incident diagnostics, and operational effectiveness.
Ability to lead through technical influence, establish architectural direction and guardrails, resolve competing technical viewpoints, and drive alignment across Product, Engineering, Security, Data, and platform teams without relying on direct authority.
Ability to simplify complex technology landscapes, identify reusable capabilities and patterns, reduce unnecessary architectural variation, and avoid over-engineering.
Strong communication and influencing skills, including explaining complex technical concepts to technical and nontechnical stakeholders.
Ability to facilitate fact-based discussions on controversial topics and sustain sound architecture decisions under delivery pressure.
Ability to balance delivery velocity, AI-native development, technical excellence, and enterprise governance requirements.

Nice To Haves

Master’s degree in a relevant technical or business discipline.

Responsibilities

Own architecture for new cloud and AI products and the overarching platform, using a consistent cloud-first, API-first, and agentic-first approach.
Define architecture standards and technical guardrails, and make decisions that account for scalability, maintainability, security, quality, and technical risk.
Analyze existing products, platforms, and architectures and develop pragmatic evolution and migration paths that account for existing investments and dependencies.
Partner with Product and Engineering, conduct architecture, design, and security reviews, and advocate for sound technical decisions when stakeholder interests differ.
Ensure solutions meet internal architecture, security, data protection, risk, compliance, and governance requirements, collaborating with relevant control and governance functions.
Define production-grade AI and agentic architecture patterns covering context engineering, evaluation, observability, performance, security, governance, traceability, and cost optimization.
Define integration patterns across applications, data, APIs, AI models, and agents, including agent-to-agent and Model Context Protocol (MCP)-based interactions where appropriate.
Establish AI evaluation and validation patterns for quality, reliability, performance, security, and regression throughout the product lifecycle.
Ensure application and data architectures support AI-enabled products through scalable data access, data quality, lineage, governance, and appropriate batch, real-time, and event-driven patterns.
Create actionable, version-controlled architecture artifacts using Architecture-as-Code practices, including reference architectures, specifications, architecture decision records, integration contracts, and reference implementations that teams can use and evolve through delivery.