Principal Applied AI Engineer

Abrigo•Raleigh, NC

14h

About The Position

Abrigo is seeking a Principal Applied AI Engineer to shape how AI capabilities are built and operated across the engineering organization. This hands-on technical leadership role designs and delivers production agent systems combining models, retrieval, tools, and dependable automation to reduce engineering friction and operational toil. The role builds reference implementations, reusable frameworks, and practical standards, while helping teams adopt AI safely and effectively through hands-on building, reviews, and mentoring. In partnership with the AI Engineering Lead and engineering leadership, the Principal Applied AI Engineer translates strategy into scalable architecture, governance, and measurable outcomes that improve developer productivity, infrastructure delivery, and operational effectiveness. The position may be based in Raleigh, North Carolina; Austin, Texas; or remotely, and reports to the Senior Manager, Platform Engineering. Occasional travel for team events is required.

Requirements

Principal-level experience leading architecture and delivery of complex software, platform, or infrastructure capabilities used across teams.
At least 8 years of experience in software, platform, or infrastructure engineering, including substantial technical leadership and production applied AI delivery.
Production experience with LLM workflows or agentic automation, including structured outputs, tool calling, retrieval, context management, and orchestration; sound judgment to use simpler automation when appropriate.
Strong Python and backend engineering skills, plus experience building APIs and integrations with developer platforms, repositories, CI/CD, and observability systems.
Experience making AI systems dependable through evaluations and regression testing, workflow monitoring, retries and recovery, version control, rollout and rollback, and operational ownership.
Practical knowledge of AI security risks and controls, including prompt injection, data exposure, permissions, secrets, and traceable actions.
Working knowledge of AWS services such as IAM, Lambda, S3, or Bedrock.
Ability to turn ambiguous needs into clear technical direction and cross-team adoption.

Nice To Haves

Experience in platform engineering, DevOps, cloud operations, or distributed systems.
Experience with Terraform or other infrastructure-as-code tools, container platforms, GitHub Actions, or automation in regulated or security-sensitive environments.

Responsibilities

Lead architecture and delivery for applied AI initiatives across Platform Engineering, Cloud Operations, and product teams.
Build reusable agent frameworks, tool interfaces, skills, and reference implementations for teams to adopt.
Design reliable workflows with explicit state, recovery, bounded autonomy, and risk-appropriate human approval.
Establish production practices for evaluation, observability, security, reliability, cost, versioning, and controlled rollout.
Embed least privilege, permission-aware retrieval, tenant isolation, auditability, and safe tool execution into shared patterns.
Evaluate models, frameworks, and build-versus-buy options using evidence about quality, security, maintainability, latency, and cost.
Influence technical direction across teams through design reviews, clear decisions, mentorship, and hands-on delivery.
Travel occasionally for team events to support engagement, relationship-building, and key decision-making.