About the Role:
The Principal Engineer / Technical Lead (AI Capabilities) is responsible for the technical design, delivery, integration, and operational readiness of AI-enabled sourcing and procurement capabilities.
Working across initiatives such as Compare Tool, Smart Intake, SourceIQ, and future AI solutions, the role serves as the primary technical owner throughout the capability lifecycle, ensuring solutions are designed, built, integrated, tested, deployed, and supported in alignment with business requirements and enterprise standards.
The role provides technical leadership across internal and external delivery teams, translating business requirements into scalable technical solutions while overseeing integrations, application development, data flows, testing, release activities, and ongoing technical support. Acting as the key technical counterpart to business, technology, and vendor stakeholders, the Technical Lead ensures AI-enabled capabilities remain secure, maintainable, and fit for purpose as they scale. The role requires experience designing and delivering AI- and agent-based solutions, including understanding how decision logic, context, and structured knowledge models interact across workflows and system architecture.
Responsibilities:
- Lead the technical design, delivery, and ongoing operation of AI-enabled sourcing and procurement capabilities, ensuring solutions remain scalable, secure, maintainable, and aligned to business requirements.
- Translate business requirements into technical solutions and oversee the end-to-end delivery lifecycle, including design, development, testing, deployment, release management, and ongoing support.
- Provide technical leadership across internal engineering resources and external delivery partners, ensuring technical quality, architectural consistency, and successful delivery of capability enhancements and new solutions.
- Design and support implementation of AI- and agent-based workflows, ensuring system behavior aligns to expected decision outcomes and user interactions
- Design, review, and maintain application code, integrations, APIs, workflows, and supporting technical components while leveraging AI-assisted development approaches to improve delivery speed and engineering productivity.
- Design and support complex, context-driven workflows where outcomes depend on multiple inputs, decision paths, and business scenarios
- Ensure solutions are designed for reuse across multiple AI-enabled capabilities (Compare, Intake, SourceIQ), avoiding tool-specific implementations
- Manage the ongoing maintenance and support of deployed capabilities, including defect resolution, technical enhancements, environment management, and operational stability.
- Ensure effective integration with enterprise platforms, data sources, and third-party solutions while maintaining alignment with enterprise architecture, security, and governance standards.
- Ensure all AI-enabled solutions consume and operate against the shared semantic layer (taxonomy, ontology, structured knowledge models) as the source of truth
- Ensure clear separation between semantic logic (definitions, classifications, decision rules), application logic, and agent behavior
- Identify and prevent inappropriate embedding of business decision logic within application code, prompts, or agent workflows
- Support testing, validation, and troubleshooting of AI-enabled solutions, including analysis of outputs, identification of edge cases, and resolution of issues related to data, logic, or system behavior
- Identify and manage technical risks, dependencies, performance issues, and scalability considerations, supporting the long-term sustainability and evolution of AI-enabled capabilities.
- Evaluate emerging technologies, engineering practices, and AI development approaches to continuously improve capability delivery, operational effectiveness, and technical outcomes.
Required Skills and Qualifications:
- 12+ years of software development experience with at least 5 years in an AI environment
- Strong software engineering foundation, particularly Python, APIs/integrations, cloud/application architecture and sufficient full-stack experience to work within a production codebase
- Practical experience with LLM, RAG, agentic AI and/or intelligent workflow solutions
- Experience owning enterprise applications through build, deployment and ongoing operation
- Ability to troubleshoot across application code, integrations, workflows, data and AI behaviour, making changes directly where appropriate and identifying when deeper specialist support is required
- Experience providing technical oversight of engineering teams and external delivery partners
- Strong technical aptitude to review solutions and code and ensure we can operate and evolve the capabilities internally rather than remaining dependent on implementation partners
- Strong hands-on experience designing, developing, and maintaining enterprise applications, integrations, APIs, workflows, and digital solutions
- Experience working with modern software development frameworks, cloud technologies, and application architectures
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.
