Principal Enterprise Software Engineer

About the role

Wolters Kluwer is seeking a hands-on Principal AI Solutions Engineer in our Architecture & Engineering Services (AES) Team to architect, build, and deploy enterprise-scale AI solutions on our next-generation AI platform. Working within our Engineering team and collaborating closely with customers, Cloud Operations, Security, Compliance, and business stakeholders, this principal engineer will drive AI-powered transformation initiatives across the enterprise.

Our Principal AI Solutions Engineer will lead the development of intelligent, cloud-native, agentic AI solutions that operationalize decision-making and process automation across the organization. The role requires deep expertise in designing AI agents, composing orchestrations, engineering reusable skills, and building extensible plugins that solve complex business problems. Beyond architecting AI systems, this engineer will demonstrate thought leadership in translating business processes into AI-driven workflows and will play a key role in evolving our AI platform roadmap.

Key Responsibilities:

Translate business processes and operational workflows into AI use cases solved via agents, orchestrations, skills, and plugins on our platform

Lead the design and delivery of end-to-end AI solutions that integrate process engineering principles with business logic

Architect agentic systems that combine intent-driven decision-making, dynamic authorization, and controlled execution

Accelerate AI adoption across the organization by democratizing access to AI capabilities, empowering business units and engineering teams to build, deploy, and operationalize intelligent workflows without deep AI expertise

Mentor the engineering team in AI solution design patterns, governance frameworks, and best practices

Shape the AI platform roadmap based on customer needs, operational requirements, and emerging patterns

Collaborate with stakeholders to define AI governance, compliance, and risk controls for enterprise deployments

Build and operationalize reusable, composable skills, orchestrations, and patterns that amplify team velocity and reduce time-to-value for downstream adopters

This role demands an engineer who understands both the technical depth of AI systems and the business context they operate within—and who is committed to scaling AI impact across the enterprise.

What YOU’ll have

Required Qualifications & Mindset

Core Motivation & Approach:

Driven to transform enterprise operations through AI agents and intelligent automation

Passionate about democratizing AI—making advanced capabilities accessible to business units and non-expert engineering teams

Motivated by solving complex, real-world process problems with scalable, resilient solutions

"Can-do" attitude; comfortable designing and shipping solutions under pressure and ambiguity

Technical Foundation:

Strong grasp of cloud platforms and hybrid environments; comfortable architecting for Azure, AWS, or GCP

Deep understanding of distributed systems, microservices architecture, and cloud-native patterns

Proven experience with high-level programming languages (Python preferred; Java/C# acceptable)

Strong fundamentals: OS concepts, storage solutions, networking, and infrastructure principles

AI/LLM & Agentic Systems:

Proven track record building production LLM applications and agentic systems

Deep expertise designing and orchestrating AI agents that make decisions, take actions, and execute workflows autonomously

Proficiency in Python; strong experience with AI frameworks and SDKs (LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, or equivalent)

Understanding of intent-driven decision-making, dynamic authorization models, and controlled execution patterns

Experience building reusable, composable AI components (skills, orchestrations, plugins) that democratize AI across teams

Process Engineering & Business Translation:

Ability to translate business processes and operational workflows into AI-solvable use cases

Skilled at decomposing complex workflows into orchestrations, skills, and plugins that work together

Comfort with process automation platforms, workflow engines, and integration patterns

Experience collaborating with business stakeholders to identify automation opportunities and design intelligent workflows

API, Integration & Architecture:

Expert in designing and consuming APIs; strong background with async patterns and event-driven architectures

Experience designing scalable integration patterns that connect AI solutions to enterprise systems

Comfortable with hybrid and multi-cloud integration challenges

Testing, Quality & Governance:

Strong background in testing AI/ML systems (unit, integration, evaluation); familiar with model benchmarking and validation frameworks

Deep experience designing AI governance frameworks, policy enforcement layers, and risk classification models

Skilled at building audit trails, compliance controls, and observability for regulated industries

Committed to quality; driven to continuously improve solutions, processes, and platform capabilities

Cloud & Data Architecture:

Deep experience with Azure services (Azure OpenAI, Azure AI Services, Cosmos DB, Functions, Azure Cognitive Search) or equivalent on AWS/GCP

Understanding of data pipelines, vector databases (Azure Cognitive Search), and knowledge graph construction

Experience designing data governance and knowledge management for AI systems

Collaboration & Leadership:

Cross-functional mindset; comfortable working with business stakeholders, customers, security, and compliance teams

Skilled at translating between business problems and technical solutions

Mentoring mindset; driven to grow the engineering team's AI capabilities and design patterns

Thought leadership in AI solution architecture, governance, and best practices

Execution & Continuous Learning:

Not afraid to prototype, test, and ship quickly; able to hack scalable solutions under pressure

Strong problem-solving skills; driven to fix bugs and continuously improve existing solutions

Comfortable with ambiguity and emerging AI patterns; eager to pioneer new approaches

Resilience and "can-do" attitude toward technical challenges and organizational complexity

RESPONSIBILITIES Organizational AI Maturity & Knowledge Transfer: Design and execute a strategic roadmap for scaling AI adoption across the enterprise—moving from pilots to production at scale Build organizational capability by creating reusable patterns, reference architectures, and best practices that multiply team velocity Mentor and upskill engineering teams in agentic systems, orchestrations, and AI solution design—transforming AI from specialist domain to accessible practice Establish AI solution maturity models that help business units and engineering teams assess readiness and accelerate time-to-value Democratize AI across the organization by building and training teams on composable skills, orchestrations, and plugins that abstract complexity Platform-Led Training & Education: Leverage the AI platform itself as a learning and experimentation environment—enabling teams to build, test, and deploy solutions in a safe, governed sandbox Design hands-on workshops, labs, and playbooks that guide teams from business problems to deployed AI solutions Create and maintain solution templates and reference implementations that serve as both learning tools and accelerators for new projects Document and evangelize emerging patterns, governance frameworks, and best practices across the organization

Strategic Research & Technology Innovation:

Research, propose, and implement emerging AI technologies and paradigms (agentic orchestration, safety patterns, cost optimization) that improve platform resilience, scalability, and economics Conduct POCs and evaluate new LLM models, frameworks, and cloud services (Azure OpenAI, newer models, optimization techniques) that advance platform capabilities Improve engineering velocity metrics, solution quality benchmarks, and AI governance maturity—quantifying the business impact of platform investments

Solution Architecture & Governance:

Produce engineering design artifacts, functional and design specifications that translate business workflows into agentic solutions Implement and advocate for AI safety patterns (output validation, escalation gates, controlled execution, rollback mechanisms) across all solutions Design AI governance frameworks aligned with NIST AI RMF and ISO/IEC 42001 for regulated industries (healthcare, financial services, legal tech) Ensure compliance with SOC 2, ISO 27001, HIPAA, and other regulatory requirements in all AI deployments Agentic Systems Development:

Architect and deliver agentic orchestration systems (multi-agent systems, tool use, long-running workflows, intent-driven decision-making) Implement AI safety and governance patterns that enable responsible, auditable agent autonomy Optimize production LLM applications for cost and performance (prompt caching, model quantization, intelligent batching) Design solutions for regulated industries that meet stringent compliance and audit requirements Platform Evolution & Thought Leadership:

Participate in strategic planning sessions to shape the AI platform roadmap based on emerging needs, customer feedback, and industry trends Identify and advocate for new processes, methodologies, and governance paradigms that make AI solutions more resilient, economical, and trustworthy Contribute to industry conversations on AI safety, governance, and responsible deployment—potentially through open-source contributions, research, or speaking engagements Pilot new technologies, validate cloud provider solutions, and propose innovations that strengthen platform capabilities and organizational AI maturity

EXPERIENCE & SKILLS REQUIRED Organizational AI Maturity & Knowledge Transfer: Design and execute a strategic roadmap for scaling AI adoption across the enterprise—moving from pilots to production at scale

Build organizational capability by creating reusable patterns, reference architectures, and best practices that multiply team velocity

Mentor and upskill engineering teams in agentic systems, orchestrations, and AI solution design—transforming AI from specialist domain to accessible practice

Establish AI solution maturity models that help business units and engineering teams assess readiness and accelerate time-to-value

Democratize AI across the organization by building and training teams on composable skills, orchestrations, and plugins that abstract complexity

Platform-Led Training & Education: Leverage the AI platform itself as a learning and experimentation environment—enabling teams to build, test, and deploy solutions in a safe, governed sandboxDesign hands-on workshops, labs, and playbooks that guide teams from business problem to deployed AI solutionsCreate and maintain solution templates and reference implementations that serve as both learning tools and accelerators for new projects

Document and evangelize emerging patterns, governance frameworks, and best practices across the organization undefined

Strategic Research & Technology Innovation: Research, propose, and implement emerging AI technologies and paradigms (agentic orchestration, safety patterns, cost optimization) that improve platform resilience, scalability, and economics

Conduct POCs and evaluate new LLM models, frameworks, and cloud services (Azure OpenAI, newer models, optimization techniques) that advance platform capabilities

Improve engineering velocity metrics, solution quality benchmarks, and AI governance maturity—quantifying the business impact of platform investments undefined

Solution Architecture & Governance: Produce engineering design artifacts, functional and design specifications that translate business workflows into agentic solutions

Implement and advocate for AI safety patterns (output validation, escalation gates, controlled execution, rollback mechanisms) across all solutionsDesign AI governance frameworks aligned with NIST AI RMF and ISO/IEC 42001 for regulated industries (healthcare, financial services, legal tech)Ensure compliance with SOC 2, ISO 27001, HIPAA, and other regulatory requirements in all AI deployments undefined

Agentic Systems Development: Architect and deliver agentic orchestration systems (multi-agent systems, tool use, long-running workflows, intent-driven decision-making)Implement AI safety and governance patterns that enable responsible, auditable agent autonomy

Optimize production LLM applications for cost and performance (prompt caching, model quantization, intelligent batching)Design solutions for regulated industries that meet stringent compliance and audit requirements undefined Platform Evolution & Thought Leadership: Participate in strategic planning sessions to shape the AI platform roadmap based on emerging needs, customer feedback, and industry trends

Identify and advocate for new processes, methodologies, and governance paradigms that make AI solutions more resilient, economical, and trustworthy Contribute to industry conversations on AI safety, governance, and responsible deployment—potentially through open-source contributions, research, or speaking engagements Pilot new technologies, validate cloud provider solutions, and propose innovations that strengthen platform capabilities and organizational AI maturity

EDUCATION: Bachelor's degree in Computer Science, Information Systems, or a related technical field (required) Master's degree in Computer Science, Engineering, Business Administration, or related field (strongly preferred) PhD in Computer Science, Machine Learning, or related discipline (a plus)

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:

$133,400.00 - $238,400.00 USD

This role is eligible for Bonus.

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.