Principal AI Systems Engineer — C++ / Applied AI

The Opportunity 

We are looking for a Principal AI Systems Engineer with deep C++ expertise to help build the next generation of AI-enabled product and platform capabilities. 

This role sits at the intersection of large-scale systems engineering, applied AI, and production software architecture. You will design and build the native infrastructure, service integration layers, evaluation systems, and reliability mechanisms that allow AI-powered features to operate safely, predictably, and efficiently inside complex software products. 

This is not a research-only role and not a prompt-engineering role. This is a hands-on principal engineering role for someone who can move between architecture, production code, AI system design, technical strategy, and cross-team leadership. 

The ideal candidate is a strong C++ engineer first, with practical AI fluency: someone who understands how modern AI systems behave, where they fail, how to integrate them into production workflows, and how to design systems that make AI useful, reliable, observable, and secure. 

What You’ll Do 

AI-Native Systems Architecture 

• Design and build native C++ infrastructure that connects complex product codebases to AI-powered services, agents, and model-backed workflows. 

• Define clean execution interfaces, schemas, validation layers, and error-handling contracts for AI-driven actions. 

• Create reliable bridges between product capabilities, AI orchestration systems, and backend services. 

• Ensure AI-initiated actions behave safely, predictably, and consistently within existing product workflows. 

• Guide long-term architecture decisions through ADRs, design documents, technical reviews, and cross-functional alignment. 

C++ Platform and Integration Engineering 

• Build high-quality C++ components for performance-sensitive, cross-platform environments. 

• Own critical client-side infrastructure such as service connectivity, session lifecycle, authentication, TLS, reconnection, concurrency, and async execution. 

• Design APIs and abstractions that are maintainable, testable, and scalable across multiple product surfaces. 

• Improve code health through modernization, refactoring, better testing, and stronger engineering patterns. 

• Balance performance, memory safety, reliability, backward compatibility, and developer experience in a mature codebase. 

Applied AI Reliability and Evaluation 

• Design systems that make AI features measurable, debuggable, and production-ready. 

• Build evaluation frameworks for AI workflows, including automated task execution, output validation, regression testing, scoring, and human review loops. 

• Define guardrails for AI-driven actions, including safety checks, capability boundaries, fallback paths, and failure handling. 

• Create privacy-conscious tracing, observability, and diagnostics for model-backed systems. 

• Partner with product, data science, security, legal, and AI governance teams to ensure AI capabilities meet quality, safety