Senior Product Software Engineer Ai Native

Join us at Wolters Kluwer and be part of a dynamic integral technology company that makes a difference every day.
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We're innovators with impact.
We provide expert software and information solutions that the world's leading professionals rely on, in the moments that matter most. We are currently looking for a Senior Product Software Engineer

- AI Native to join our team.

About the Role
The Senior Product Software Engineer (AI native) will own software solutions from discovery through production, translating customer and business needs into secure, reliable, scalable, and maintainable software products. Takes ambiguous team-level problems and works across stakeholders to define requirements, shape solution approaches, coordinate dependencies, and deliver successful outcomes. Exercises sound engineering judgment and uses approved AI-assisted development and automation tools where appropriate, while remaining accountable for the quality, correctness, security, and operational readiness of all work approved or released. Serves as a technical resource within the team and helps strengthen the capability of less experienced engineers. Essential Duties and

Responsibilities:
End-to-End Solution Ownership: Own software solutions from discovery through production, translating customer, business, and technical requirements into sustainable outcomes. Problem Definition and Solution Design: Analyze ambiguous requirements, clarify objectives, evaluate alternatives, and determine solution strategies. Software Development: Design, develop, test, implement, maintain, and improve software applications, services, and platforms using direct and approved AI-assisted approaches. AI-Assisted Engineering: Apply approved AI-assisted techniques while validating outputs and maintaining accountability for architecture, quality, security, and operational readiness. Specification Development: Document requirements, design approaches, acceptance criteria, dependencies, risks, and implementation considerations. Verification and Validation: Determine and execute testing and verification approaches based on complexity, risk, and business impact. Technical Guidance: Provide technical guidance through design reviews, code reviews, knowledge sharing, mentoring, and engineering best practices. Troubleshooting and Production Support: Diagnose and resolve cross-component, production, and system-level issues and implement sustainable corrective actions. Security and Compliance: Apply security, privacy, compliance, and data-protection requirements throughout the lifecycle. Product and Stakeholder Collaboration: Partner with product, design, architecture, operations, security, and business stakeholders to shape solutions. Performance and Reliability Improvement: Improve performance, scalability, reliability, resiliency, and maintainability while reducing operational risk. Continuous Improvement: Improve engineering processes, delivery effectiveness, and team capability through automation, standardization, and knowledge sharing. Customer and Business Awareness: Consider customer impact, product strategy, operational implications, and business outcomes in technical decisions. AI-Native Engineering Accountability: Applies specification-driven and evaluation-driven approaches to ambiguous work. Provides context, constraints, and evaluation criteria, validates generated output, and coaches others on appropriate responsible use. Verification and Quality Accountability: Every engineer remains accountable for the work approved or released, regardless of whether the work was produced directly or with approved AI assistance. AI-generated output remains unverified input until it has been reviewed and validated through practices appropriate to the associated risk. Determine the testing and validation needed within the assigned area. Ensure solutions meet quality, reliability, security, compliance, and operational expectations. Define and evaluate acceptance criteria.

Advanced Technology Adoption and Utilization: Applies specification-driven and evaluation-driven approaches to ambiguous work. Provides context, constraints, and evaluation criteria, validates generated output, and coaches others on appropriate responsible use. Competencies: Ownership Mindset: Acts like an owner, takes accountability for outcomes beyond formal role boundaries, and creates disproportionate business value while respecting security, regulatory, financial, and delegated decision-making controls. Organizational Velocity: Increases speed by removing friction, simplifying decisions, and accelerating execution. Technical Innovation & Leverage: Creates innovative solutions whose impact far exceeds the effort required to build them. Engineering Leverage: Improves engineering productivity through tools, platforms, frameworks, and automation. A