Principal Enterprise Software Engineer

Wolters Kluwer•Kennesaw, GA

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 organization. The engineer will lead the development of intelligent, cloud-native, agentic AI solutions that support decision-making and process automation. This includes designing AI agents, orchestrations, reusable skills, and extensible plugins; translating business processes into AI-driven workflows; and contributing to the evolution of the AI platform roadmap. The role also focuses on scaling AI adoption, enabling teams to build and operationalize governed solutions, and advancing responsible AI practices for regulated industries. The position requires a bachelor’s degree in Computer Science, Information Systems, or a related technical field. A master’s degree is strongly preferred, and a PhD is a plus. Compensation is listed as $133,400–$238,400 USD, with eligibility for a bonus. The position’s primary location and workplace arrangement are not specified.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or a related technical field.
Strong grasp of cloud platforms and hybrid environments, with architecture experience involving Azure, AWS, or GCP.
Deep understanding of distributed systems, microservices architecture, and cloud-native patterns.
Experience with a high-level programming language; Python preferred, with Java or C# acceptable. Python proficiency is specifically required for AI frameworks and SDKs.
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.
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 such as skills, orchestrations, and plugins.
Ability to translate business processes and operational workflows into AI-solvable use cases and decompose complex workflows into coordinated components.
Experience with process automation platforms, workflow engines, and integration patterns, and collaboration with business stakeholders to identify automation opportunities.
Expertise designing and consuming APIs, including async patterns and event-driven architectures.
Experience designing scalable integrations that connect AI solutions to enterprise systems and working through hybrid and multi-cloud integration challenges.
Strong background testing AI/ML systems, including unit, integration, and evaluation testing; familiarity with model benchmarking and validation frameworks.
Experience designing AI governance frameworks, policy enforcement layers, and risk classification models.
Ability to build 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 AWS/GCP services.
Understanding of data pipelines, vector databases, and knowledge graph construction.
Experience designing data governance and knowledge management for AI systems.
Cross-functional collaboration skills, including translating between business problems and technical solutions and working with business, customer, security, and compliance stakeholders.
Mentoring ability and interest in developing engineering teams’ AI capabilities and design practices.
Strong problem-solving skills, willingness to prototype and ship quickly, and comfort working under pressure, ambiguity, and evolving AI patterns.

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 with Azure OpenAI and other Azure AI and data services, or equivalent AWS/GCP services.
Experience with prompt caching, model quantization, and intelligent batching.
Potential contributions to industry conversations through open-source contributions, research, or speaking engagements.

Responsibilities

Translate business processes and operational workflows into AI use cases implemented through 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 combining intent-driven decision-making, dynamic authorization, and controlled execution.
Scale organizational AI adoption by enabling business units and engineering teams to build, deploy, and operationalize intelligent workflows.
Mentor engineering teams 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 reusable, composable skills, orchestrations, and patterns to improve team velocity and reduce time-to-value.
Create a strategic roadmap for scaling AI adoption from pilots to production across the enterprise.
Develop reusable patterns, reference architectures, best practices, and AI solution maturity models; mentor and upskill teams.
Use the AI platform as a safe, governed learning and experimentation environment; design workshops, labs, playbooks, templates, and reference implementations.
Research and implement emerging AI technologies and paradigms; conduct proofs of concept and evaluate 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, functional specifications, and design specifications that translate business workflows into agentic solutions.
Implement AI safety patterns, including output validation, escalation gates, controlled execution, and rollback mechanisms.
Design governance frameworks aligned with NIST AI RMF and ISO/IEC 42001, and ensure deployments meet applicable regulatory requirements, including SOC 2, ISO 27001, and HIPAA.
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 using techniques such as prompt caching, model quantization, and intelligent batching.
Participate in strategic planning and advocate for processes, methodologies, and governance approaches that make AI solutions more resilient, economical, and trustworthy.
Contribute to industry discussions on AI safety, governance, and responsible deployment, potentially through open-source work, research, or speaking engagements.
Pilot new technologies, validate cloud-provider solutions, and propose innovations to strengthen platform capabilities and organizational AI maturity.