Principal AI Engineer
Quick Summary
Azure Kubernetes/AKS Distributed systems Microservices Event-driven architectures Large-scale platform modernization Strong hands-on software engineering experience with: C# .
We are seeking a Principal AI Engineer to define and lead the AI architecture for a large-scale Audit & Assurance application portfolio spanning engagement workflows, evidence management, analytics, content intelligence, risk assessment, compliance, and review automation.
This is a highly hands-on technical leadership role for an individual who can bridge modern software architecture, AI engineering, cloud-native platforms, distributed systems, and enterprise governance. You will architect AI-enabled systems that operate at enterprise scale across multiple products, regions, and regulatory environments.
You will be responsible for establishing the target-state AI architecture, reference patterns, engineering standards, platform capabilities, and implementation guardrails for AI-assisted audit workflows. The role requires deep expertise in agentic AI systems, retrieval architectures, knowledge graphs, platform modernization, security-by-design, and large-scale SaaS architecture.
You are expected to design, build, prototype, review code, establish architectural direction, and coach engineering teams while remaining actively engaged in implementation and technical decision-making.
The scope spans all Audit & Assurance products across North America and Europe.
Responsibilities
~1 min read- Define the enterprise AI architecture strategy across the Audit & Assurance portfolio.
- Establish reference architectures for:
- Agentic workflows
- Multi-agent orchestration
- Retrieval-augmented generation (RAG)
- Knowledge graphs
- Semantic search
- AI-assisted content generation
- AI-supported analytical workflows
- Create reusable AI platform capabilities consumed across multiple products.
- Architect end-to-end solutions across:
- Audit engagement lifecycle
- Planning and risk assessment
- Evidence collection and validation
- Compliance and standards interpretation
- Analytics and anomaly detection
- Quality review and sign-off workflows
- Establish bounded contexts, domain-driven design principles, API strategies, and event contracts.
- Design scalable multi-agent systems using:
- Manager–Executor
- Planner–Executor
- Blackboard
- Event-driven agent collaboration
- Human-in-the-loop workflows
- Define memory, context, tool usage, routing, evaluation, and safety frameworks.
- Lead architecture reviews and design governance.
- Produce Architecture Decision Records (ADRs).
- Coach engineering teams in:
- AI architecture
- Distributed systems
- Performance engineering
- Reliability engineering
- Architecture trade-off analysis
- Act as the technical authority for AI architecture decisions.
Requirements
~1 min read- 15+ years in software engineering and architecture.
- 7+ years designing large-scale SaaS and distributed systems.
- Proven experience architecting enterprise AI systems in production.
- Deep expertise in:
- Azure
- Kubernetes/AKS
- Distributed systems
- Microservices
- Event-driven architectures
- Large-scale platform modernization
- Strong hands-on software engineering experience with:
- C#
- .NET
- Python
- APIs
- Distributed systems
- Demonstrated success designing AI-enabled enterprise applications.
Practical experience with:
- Multi-agent architectures
- LangGraph
- Semantic Kernel
- Microsoft Foundry Agent Service
- Model Context Protocol (MCP)
- Agent orchestration frameworks
- Offline evaluations
- Golden datasets
- Confidence scoring
- Hallucination mitigation
- Human approval workflows
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.
What We Offer
~1 min readThis 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.
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.
Location & Eligibility
About this job
- Pay per year
- $107,500–$188,400 a year
- 29% below the $208,575 median for Machine Learning Engineer jobs on Jobera (1,021 salaries).
- Who can apply
- United States
- Posted
- 15 days ago
Listing Details
- Posted
- October 26, 2026
- First seen
- November 7, 2026
- Last seen
- November 10, 2026
Posting Health
- Days active
- 2
- Repost count
- 0
- Trust Level
- 31%
- Scored at
- November 10, 2026
Signal breakdown
Similar Machine Learning Engineer jobs
View all →Stay ahead of the market
Get the latest job openings, salary trends, and hiring insights delivered to your inbox every week.
No spam. Unsubscribe at any time.