Principal AI Engineer

Our client, a global leader in claims management and the largest third-party administrator (TPA) in the world, is actively looking for a Principal AI Engineer to join their team! This is a 100% remote role however working hours will be in CST.

This is a greenfield opportunity to help a $21 billion organization build its enterprise AI platform and leverage Generative AI to improve customer experience and transform claims and managed care operations.

The Principal AI Engineer will own the technical outcomes of a value stream, working directly with transformation leadership and business users to shape problems, architect solutions, and deliver production-ready AI applications. This is a hands-on technical leadership role for an experienced engineer who has owned and operated production GenAI systems in real-world environments .

The ideal candidate combines deep full-stack engineering expertise with strong architectural judgment, executive-level communication skills, and the ability to balance rapid delivery with long-term platform strategy. This is an individual contributor role, not a people management position.

Responsibilities

Technical Ownership & Architecture: Own technical outcomes from problem definition through production deployment and ongoing operation, making architectural decisions that balance delivery speed, scalability, and long-term maintainability.

Generative AI Engineering

Architect and personally build production AI agents, workflows, and applications using Python, React, and enterprise integrations.

Cloud Infrastructure & DevOps

Set engineering standards for AWS, Terraform, and GitHub Actions, including deployment practices, infrastructure management, CI/CD, and operational recovery.

AI Quality & Governance

Establish evaluation, observability, provenance, failure handling, and human review controls to ensure AI applications are reliable, secure, and appropriate for production use.

Platform Strategy & Integration

Guide decisions around shared AI platform capabilities, prevent duplicative development, and ensure solutions align with enterprise architecture, security, and compliance standards.

Business & Executive Partnership

Work directly with transformation leadership, claims adjusters, nurse reviewers, and operations teams to shape ambiguous business problems into actionable engineering solutions.

Vendor & Cross-Functional Collaboration

Partner with internal platform teams and external engineering vendors to establish technical standards, shared development practices, and clear delivery expectations.

Technical Leadership & Mentorship

Mentor Senior and Junior AI Engineers, lead design reviews, resolve complex technical issues, and promote engineering excellence across the team. Required Skills & Experience Bachelor’s degree in Computer Science, Engineering, or a related quantitative discipline is required; master’s degree preferred.

10+ years of professional software engineering experience, including at least 4 years owning production Generative AI or applied AI systems. Demonstrated experience building and operating production AI agents, workflows, or applications , including evaluation, failure handling, and human-in-the-loop review. Prototype- or demo-only experience does not qualify.

Expert hands-on experience with Python, React, AWS, GitHub Actions, and Terraform , with the ability to establish engineering standards and review others’ work. Strong knowledge of APIs, SQL, service design, AI orchestration, tool use, evaluation, and observability. Excellent communication and collaboration skills, with the ability to influence executive stakeholders, business partners, architects, and engineering teams.

Experience mentoring engineers and leading technical delivery without relying on formal people-management authority.

Preferred Qualifications

• Experience in insurance, claims, healthcare, or another regulated industry.
• Experience with machine learning, model fine-tuning, or graph databases such as Neo4j.
• Familiarity with PydanticAI or similar agent frameworks, agent observability, AIOps, or LLMOps.