Principal Applied AI Engineer

We provide technology that community financial institutions use to manage risk and drive growth. Our solutions automate key processes – from anti-money laundering to fraud detection to CECL readiness to lending workflows – empowering our customers by addressing their Enterprise Risk Management needs.

Abrigo is looking for a Principal Applied AI Engineer to shape how we build and operate AI capabilities across our engineering organization. In this hands‑on technical leadership role, you’ll design and deliver production agent systems that combine models, retrieval, tools, and dependable automation to reduce engineering friction and operational toil.

You’ll build reference implementations, establish reusable frameworks and practical standards, and help teams adopt AI safely and effectively by building, reviewing, and mentoring alongside them. Partnering with the AI Engineering Lead and engineering leadership, you’ll translate strategy into scalable architecture, governance, and measurable outcomes that improve developer productivity, infrastructure delivery, and operational effectiveness. This position can be based in Raleigh, NC, Austin, TX, or remote and will report to the Senior Manager, Platform Engineering.

• 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 that teams can adopt.
• Design reliable workflows with explicit state, recovery, bounded autonomy, and human approval suited to the risk.
• Set 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 quality, security, maintainability, latency, and cost evidence.
• Influence technical direction across teams through design reviews, clear decisions, mentorship, and hands‑on delivery.
• Occasional travel required for team events to support engagement, relationship‑building, and key decision‑making.
• You’ll know you’re making an impact when teams adopt shared patterns, AI workflows meet agreed quality and operational targets, and engineering work becomes measurably more efficient and supportable.

• Principal‑level experience leading architecture and delivery of complex software, platform, or infrastructure capabilities used across teams.
• 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 it fits.
• 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: 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, and ability to turn ambiguous needs into clear technical direction and cross‑team adoption.
• Experience in platform engineering, Dev Ops, cloud operations, or distributed systems is a plus, as is experience with Terraform or other infrastructure‑as‑code tools, container…