Lead Architect, Application & Product- AI

Wolters Kluwer•Coppell, TX

2d•$107,500 - $188,400

About The Position

As Lead Application & Product Architect, you will shape 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 pragmatic, production-grade architecture patterns that engineering teams can execute and evolve through Spec-Driven Development and Architecture-as-Code practices, while ensuring architecture decisions meet Wolters Kluwer requirements for security, data protection, risk, compliance, and governance. You will guide architecture for new products and the overarching platform, define standards and patterns, and help teams evolve existing products and architectures incrementally. The role partners with Product, Engineering, Security, Data, platform, and governance teams to make and review technical decisions. It also establishes production-grade AI and agentic patterns, integration approaches, AI evaluation practices, and the data architecture needed to support AI-enabled products. The position requires substantial software engineering and architecture experience, including hands-on experience architecting AI/ML, generative AI, or agentic capabilities in customer-facing production software. A bachelor's degree in a relevant field or equivalent practical experience is required; a relevant master's degree is preferred.

Requirements

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, and 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, with 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 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 the ability to explain complex technical concepts clearly 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 high delivery velocity, AI-native development, technical excellence, and enterprise governance requirements.
Participate in interviews without AI tools or external prompts; virtual backgrounds must be removed, and the process includes in-person interviews. Applicants may be required to appear onsite at a Wolters Kluwer office as part of recruitment.

Nice To Haves

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

Responsibilities

Own the architecture of new cloud and AI products and the overarching platform, applying a consistent cloud-first, API-first, and agentic-first approach.
Define architecture standards and technical guardrails, making 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 requirements for architecture, security, data protection, risk, compliance, and governance in collaboration 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 covering 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, including reference architectures, specifications, architecture decision records (ADRs), integration contracts, and reference implementations for engineering teams to use and evolve.