Role Summary

The Lead Product Architect is responsible for defining and driving the technical vision, architecture strategy, and engineering direction for a portfolio of products and platforms. This role provides architectural leadership across product development, modernization initiatives, AI-enabled solutions, platform engineering, and enterprise integration efforts.

The ideal candidate combines deep expertise in software architecture, cloud-native engineering, distributed systems, and AI-powered application design with a strong understanding of modern software delivery practices, including Spec-Driven Development (SDD), Agentic SDLC, AI-assisted development, and developer productivity platforms.

This individual will partner closely with Product Management, Engineering Leadership, UX, Security, DevOps, Data, and Business stakeholders to deliver scalable, secure, resilient, and future-ready solutions. In addition to shaping architecture, the role will actively guide engineering teams in adopting AI-first engineering practices and modern architecture patterns that accelerate delivery while maintaining quality, security, and governance standards.

Key Responsibilities

Product & Solution Architecture Leadership

• Define and maintain end-to-end product architecture aligned with business strategy, product roadmaps, and enterprise standards.
• Lead architecture and design reviews across multiple product initiatives and engineering teams.
• Develop architectural blueprints, solution designs, reference architectures, and technical standards.
• Ensure systems meet requirements for scalability, reliability, performance, security, maintainability, and operational excellence.
• Provide oversight for architectural governance and critical technical decisions.
• Evaluate technology options and establish long-term technical roadmaps.

AI Architecture & AI-Powered Product Engineering

• Design and govern architectures for AI-enabled products, intelligent workflows, agentic systems, conversational experiences, and retrieval-augmented generation (RAG) solutions.
• Define patterns for integrating Large Language Models (LLMs), AI agents, semantic search, vector databases, orchestration frameworks, and AI-powered business workflows.
• Establish architectural standards for AI safety, grounding, observability, evaluation, prompt management, and responsible AI practices.
• Guide teams in building AI-native applications that balance business value, reliability, security, and compliance.
• Evaluate emerging AI technologies and identify opportunities to enhance products, customer experiences, and engineering productivity.
• Support architecture and governance for enterprise AI platforms and reusable AI capabilities.

Spec-Driven Development & Agentic SDLC

• Champion Spec-Driven Development methodologies across engineering teams.
• Define architecture patterns, standards, and reusable templates that enable AI-assisted software development.
• Partner with engineering teams to create specifications that can be consumed by AI coding agents and automation platforms.
• Enable Agentic SDLC practices through architectural guardrails, AI-enabled workflows, and development automation.
• Define standards for requirements decomposition, architecture artifacts, code generation, testing automation, and deployment workflows.
• Ensure AI-generated outputs align with architectural principles, quality standards, and security requirements.

Technical Leadership & Modernization

• Lead modernization initiatives involving legacy application transformation, cloud migration, platform modernization, API strategies, and microservices adoption.
• Drive architectural improvements that reduce technical debt and improve engineering agility.
• Design and oversee implementation of cloud-native architectures and distributed systems.
• Collaborate with engineering teams to optimize performance, observability, reliability, and operational maturity.
• Guide teams on architecture patterns involving APIs, event-driven systems, integration platforms, and platform engineering capabilities.
• Promote engineering excellence through architecture reviews, design coaching, and implementation guidance.

Innovation, Prototyping & AI-Assisted Development

• Lead proof-of-concepts and architecture spikes to validate emerging technologies and innovative solution approaches.
• Drive adoption of AI-assisted development capabilities including GitHub Copilot, coding agents, automated testing, code review assistants, and developer productivity platforms.
• Establish best practices for AI-assisted engineering and AI-enabled architecture design.
• Collaborate with Product and Engineering leaders to identify opportunities for automation and engineering acceleration.
• Create reusable architectural assets, design patterns, reference implementations, and engineering frameworks.

Cross-Functional Leadership & Collaboration

• Partner with Product Management, Engineering Managers, Architects, Security, UX, QA, DevOps, and Data teams.
• Facilitate architecture reviews, technical planning sessions, and strategic technology discussions.
• Communicate architectural decisions and technical trade-offs to both technical and business audiences.
• Influence technology direction through data-driven recommendations and architectural leadership.
• Serve as a trusted advisor to engineering leadership on architecture strategy, technology investments, modernization priorities, and AI adoption.

Required Skills & Expertise

Architecture & Engineering

• Extensive experience designing enterprise-scale software products and platforms.
• Strong expertise in application architecture, solution architecture, and distributed systems design.
• Deep understanding of cloud-native architectures and modern engineering practices.
• Experience designing scalable APIs, event-driven systems, microservices, and platform architectures.
• Strong knowledge of security, resiliency, observability, performance, and operational architecture.
• Experience with enterprise integration patterns and complex system landscapes.

Cloud & Platform Technologies

• Expertise with Azure environments.
• Experience architecting cloud-native applications and platform services.
• Strong understanding of DevSecOps, CI/CD, Infrastructure as Code, and Platform Engineering.
• Experience with container technologies, Kubernetes, service meshes, and enterprise integration platforms.

AI & Modern Software Engineering

• AI-assisted software development.
• Generative AI application architecture.
• Agentic systems and AI orchestration frameworks.
• Retrieval-Augmented Generation (RAG).
• Prompt engineering and evaluation frameworks.
• AI safety, governance, and responsible AI design.
• Spec-Driven Development.
• Agentic SDLC.
• Developer productivity platforms and AI coding assistants.
• AI-powered search, recommendations, workflow automation, and intelligent document processing.

Leadership & Influence

• Strong architectural leadership and technical mentoring experience.
• Ability to influence engineering teams and stakeholders without direct authority.
• Excellent communication and stakeholder management skills.
• Capability to lead architecture initiatives across multiple teams and product areas.
• Strong analytical thinking and complex problem-solving abilities.

Preferred Qualifications

• Bachelor's or Master’s Degree in Computer Science, Software Engineering, Information Systems, AI or related technical field.
• 12+ years of software engineering and architecture experience.
• 5+ years in Solution Architect, Product Architect, or Principal Architect roles.
• Experience with .NET, Angular, MS SQL, Oracle
• Experience with AI/ML-enabled products, enterprise AI platforms, or LLM-powered solutions.
• Experience with application modernization and legacy platform transformations.
• Experience supporting enterprise SaaS and cloud-based software products.

Preferred Certifications

• Microsoft Azure Solutions Architect Expert
• TOGAF Certification
• ArchiMate Certification
• Kubernetes Certifications (CKA/CKAD)
• Azure AI Engineer Associate
• AI Architecture, Prompt Engineering, or Generative AI certifications

Competencies & Leadership Behaviors

• Strategic Thinking & Technology Vision
• Customer-Centric Innovation
• Architecture Ownership & Accountability
• Systems Thinking
• AI-First Mindset
• Data-Driven Decision Making
• Cross-Functional Leadership
• Continuous Learning & Innovation
• Engineering Excellence
• Coaching & Technical Mentorship

Our Interview Practices

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.

Compensation:

Compensation range listed is based on primary location of the position.  Actual base salary offer is influenced by a wide array of factors including but not limited to skills, experience and actual hiring location. Your recruiter can share more information about the specific offer for the job location during the hiring process. 

Additional Information:

Wolters Kluwer offers a wide variety of competitive benefits and programs to help meet your needs and balance your work and personal life, including but not limited to: Medical, Dental, & Vision Plans, 401(k), FSA/HSA, Commuter Benefits, Tuition Assistance Plan, Vacation and Sick Time, and Paid Parental Leave. Full details of our benefits are available upon request.