Principal AI Engineer

Description

The company builds Audit & Assurance software and needs a principal AI engineer to set the AI architecture across its product portfolio, which covers engagement workflows, evidence management, analytics, content intelligence, risk assessment, compliance and review automation.

The role is hands-on technical leadership: you bridge software architecture, AI engineering, cloud-native platforms, distributed systems and enterprise governance, and design AI-enabled systems that run at enterprise scale across several products, regions and regulatory settings.

You will set the target-state AI architecture, reference patterns, engineering standards, shared platform capabilities and guardrails for AI-assisted audit work. Deep knowledge of agentic AI, retrieval architectures, knowledge graphs, platform modernization, security by design and large-scale SaaS is essential.

You will design, build, prototype and review code, set architectural direction, and coach engineers while staying close to implementation and technical decisions.

The scope covers all Audit & Assurance products in North America and Europe.

Responsibilities

• Set the enterprise AI architecture strategy for the Audit & Assurance portfolio.
• Define reference architectures for agentic workflows, multi-agent orchestration, retrieval-augmented generation (RAG), knowledge graphs, semantic search, AI-assisted content generation and AI-supported analytical workflows.
• Build reusable AI platform capabilities that several products can consume.
• Architect end-to-end solutions for the audit engagement lifecycle, planning and risk assessment, evidence collection and validation, compliance and standards interpretation, analytics and anomaly detection, and quality review and sign-off.
• Define bounded contexts, domain-driven design principles, API strategy and event contracts.
• Design scalable multi-agent systems using Manager, Executor, Planner, Executor, Blackboard and event-driven collaboration, including human-in-the-loop steps.
• Define frameworks for agent memory, context, tool use, routing, evaluation and safety.
• Lead architecture reviews and design governance, and write Architecture Decision Records (ADRs).
• Coach engineering teams on AI architecture, distributed systems, performance, reliability and architecture trade-offs.
• Serve as the technical authority on AI architecture decisions.

Requirements

• 15+ years in software engineering and architecture.
• 7+ years designing large-scale SaaS and distributed systems.
• Proven record of running enterprise AI systems in production.
• Deep experience with Azure, Kubernetes/AKS, distributed systems, microservices, event-driven architectures and large-scale platform modernization.
• Strong hands-on engineering in C#,.NET, Python, APIs and distributed systems.
• Demonstrated success designing AI-enabled enterprise applications.
• Preferred: practical experience with multi-agent architectures, LangGraph, Semantic Kernel, Microsoft Foundry Agent Service, Model Context Protocol (MCP) and agent orchestration frameworks.
• Preferred: experience with offline evaluations, golden datasets, confidence scoring, hallucination mitigation and human approval workflows.

Conditions

Salary range: $107,500.00 to $188,400.00 USD, based on the primary location of the role.

The role is eligible for a bonus.

The actual base salary offer depends on factors including skills, experience and the hiring location. A recruiter can share the figure for a specific location during hiring.

Candidates may be asked to attend an interview onsite at a company office.

Candidates must complete interviews without AI tools or outside help; using them is grounds for disqualification.

Equal opportunity applies to all candidates.

Benefits

• Medical, dental and vision plans, 401(k), FSA/HSA, commuter benefits, tuition assistance, vacation and sick time, and paid parental leave.
• Full benefit details are available on request.