Principal Enterprise Software Engineer

Wolters Kluwer•Minneapolis, MN

6d•$133,400 - $238,400

About The Position

Wolters Kluwer is seeking a hands-on principal AI solutions engineer for its Architecture & Engineering Services (AES) team. The role will architect, build, and deploy enterprise-scale AI solutions on the company’s next-generation AI platform, working with engineering, customers, Cloud Operations, Security, Compliance, and business stakeholders to advance AI-powered transformation across the enterprise. The engineer will lead the development of intelligent, cloud-native, agentic AI solutions for decision-making and process automation. This includes designing AI agents and orchestrations, engineering reusable skills, and building extensible plugins to solve complex business problems. The position also translates business processes into AI-driven workflows, helps evolve the AI platform roadmap, and grows organizational capability through reusable patterns, training, governance, and thought leadership. The role includes researching and evaluating emerging AI technologies, developing production-grade agentic systems, and ensuring solutions are safe, auditable, scalable, cost-effective, and compliant with applicable standards and regulations. A bachelor's degree in a related technical field is required. A master's degree is strongly preferred, and a PhD is a plus.

Requirements

Bachelor's degree in Computer Science, Information Systems, or a related technical field.
Strong grasp of cloud platforms and hybrid environments, including experience architecting for Azure, AWS, or GCP.
Deep understanding of distributed systems, microservices architecture, and cloud-native patterns.
Proven experience with a high-level programming language; Python is preferred, with Java or C# acceptable.
Strong fundamentals in operating systems, storage, networking, and infrastructure.
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 and strong experience with AI frameworks and SDKs such as 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, including skills, orchestrations, and plugins.
Ability to translate business processes and operational workflows into AI-solvable use cases and decompose complex workflows into cooperating components.
Comfort with process automation platforms, workflow engines, integration patterns, and collaboration with business stakeholders to identify automation opportunities.
Expertise in designing and consuming APIs, with a strong background in asynchronous patterns and event-driven architectures.
Experience designing scalable integrations between AI solutions and enterprise systems, including hybrid and multi-cloud integration challenges.
Strong background in testing AI/ML systems, including unit testing, integration testing, evaluation, model benchmarking, and validation frameworks.
Deep experience designing AI governance frameworks, policy enforcement layers, and risk classification models.
Skills in building audit trails, compliance controls, and observability for regulated industries.
Experience with Azure services such as Azure OpenAI, Azure AI Services, Cosmos DB, Functions, and Azure Cognitive Search, or equivalent services on AWS or GCP.
Understanding of data pipelines, vector databases, and knowledge graph construction.
Experience designing data governance and knowledge management for AI systems.
Cross-functional collaboration skills with business stakeholders, customers, security, and compliance teams, including translating between business problems and technical solutions.
Mentoring capability and thought leadership in AI solution architecture, governance, and best practices.
Strong problem-solving skills and a commitment to quality and continuous improvement.
Comfort prototyping, testing, and shipping quickly under pressure and ambiguity, with resilience and a can-do approach to technical and organizational challenges.

Nice To Haves

Master's degree in Computer Science, Engineering, Business Administration, or a related field.
PhD in Computer Science, Machine Learning, or a related discipline.
Experience designing governance frameworks for regulated industries such as healthcare, financial services, or legal technology.
Contributions to industry conversations on AI safety, governance, and responsible deployment through open-source work, research, or speaking engagements.

Responsibilities

Translate business processes and operational workflows into AI use cases using agents, orchestrations, skills, and plugins.
Lead the design and delivery of end-to-end AI solutions integrating process engineering principles and business logic.
Architect agentic systems incorporating intent-driven decision-making, dynamic authorization, and controlled execution.
Scale AI adoption by making AI capabilities accessible to business units and engineering teams.
Mentor engineering teams in AI solution design patterns, governance frameworks, and best practices.
Shape the AI platform roadmap in response to customer needs, operational requirements, and emerging patterns.
Collaborate with stakeholders to define AI governance, compliance, and risk controls for enterprise deployments.
Build reusable, composable skills, orchestrations, and patterns that increase team velocity and reduce time-to-value.
Design and execute a roadmap for scaling enterprise AI adoption from pilots to production.
Create reusable patterns, reference architectures, best practices, and AI solution maturity models.
Develop and deliver hands-on workshops, labs, playbooks, templates, and reference implementations to help teams build and deploy governed AI solutions.
Document and promote emerging patterns, governance frameworks, and best practices across the organization.
Research and implement emerging AI technologies and paradigms to improve platform resilience, scalability, and economics.
Conduct proofs of concept and evaluate LLM models, frameworks, and cloud services.
Improve engineering velocity metrics, solution-quality benchmarks, and AI governance maturity, and quantify platform investment impact.
Produce engineering design artifacts and specifications that translate business workflows into agentic solutions.
Implement AI safety patterns, including output validation, escalation gates, controlled execution, and rollback mechanisms.
Design AI governance frameworks aligned with NIST AI RMF and ISO/IEC 42001, and support compliance with SOC 2, ISO 27001, HIPAA, and other requirements.
Architect and deliver agentic orchestration systems, including multi-agent systems, tool use, long-running workflows, and intent-driven decision-making.
Optimize production LLM applications for cost and performance, including through prompt caching, model quantization, and intelligent batching.
Participate in strategic planning and advocate for processes, methodologies, and governance paradigms that make AI solutions more resilient, economical, and trustworthy.
Contribute to industry discussions 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.