Principal AI Engineer

Required Skills & Experience

• Advanced expertise in backend engineering, distributed systems, and platform architecture.
• Experience designing, building, and scaling complex APIs and enterprise systems.
• Proven ability to modernize legacy applications and mission-critical platforms.
• Strong knowledge of API design, schema management, versioning, validation, authentication, authorization, and observability.
• Experience designing resilient, scalable systems that support high-concurrency workloads.
• Ability to define architectural standards, governance, and best practices across engineering teams.
• Experience shaping technical strategy and influencing cross-functional engineering teams.
• Strong systems-thinking mindset with the ability to assess platform-wide impacts and dependencies.
• Experience mentoring senior engineers and leading architectural design reviews.
• Strong collaboration and communication skills, with the ability to balance long-term strategy, business priorities, and delivery needs.

Nice to Have Skills & Experience

• Experience with gRPC, Protocol Buffers, GraphQL, or similar strongly typed API frameworks.
• Familiarity with Model Context Protocol (MCP), AI orchestration platforms, AI agents, or LLM integrations.
• Experience with event-driven architecture, workflow orchestration, and distributed platforms.
• Experience defining enterprise engineering standards and leading large-scale modernization efforts.
• Experience building observable systems with advanced traffic management and resiliency controls.
• Experience supporting AI-native products or machine-driven integration ecosystems.

Job Description

Insight Global is hiring a Principal AI Engineer to lead the modernization of a global technical solutions company. This person will design, build, and evolve internal AI-powered software platforms that help engineering teams understand, automate, and improve the software development lifecycle. They will build reusable tools, APIs, workflows, and interfaces that allow development teams to adopt AI-assisted development patterns without rebuilding infrastructure from scratch. They will connect data from CI/CD, deployments, approvals, security scans, quality gates, source control, work tracking, and service dependency systems into a clearer view of software change across ABC. They will also build durable infrastructure for long-running AI-assisted workflows such as code review, change analysis, release support, planning, remediation, validation, and other multi-step engineering processes. Without this role, internal AI tooling may remain fragmented, engineering teams may build one-off solutions, software change intelligence may remain unclear, and AI-assisted engineering workflows may not become reliable, observable, or broadly adopted.