Principal Enterprise Software Engineer
Wk · United States, Kennesaw, Georgia · onsite
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 adv