Senior 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 Senior AI Engineer to join their team! This is a 100% remote opportunity but working hours will be in CST. This is a contract-to-hire position with excellent potential for long-term growth! 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 streamline claims and managed care operations. Working alongside a Principal AI Engineer, the Senior AI Engineer will own development streams end to end — from shaping business problems with stakeholders to building, deploying, and operating production-ready AI applications. This role is ideal for an experienced engineer who has at least three years of hands-on experience shipping production GenAI agents, workflows, or applications and has operated the systems they built. You’ll work across the full stack, own cloud infrastructure and deployment, collaborate directly with business users and vendor engineering teams, and help guide junior engineers. This is a hands-on technical role, not a people management position. Responsibilities • End-to-End AI Engineering: Own engineering workstreams from problem definition and implementation through production deployment, monitoring, and measurement of business outcomes. • Generative AI Development: Design, build, and operate production AI agents, workflows, and applications, including orchestration, tool execution, state management, and enterprise integrations. • Full-Stack Development: Build Python backend services and APIs alongside React interfaces that integrate AI capabilities into business users’ workflows. • Cloud Infrastructure & DevOps: Deploy and operate production workloads on AWS, managing infrastructure through Terraform and CI/CD pipelines through GitHub Actions. • AI Quality & Reliability: Implement evaluation, observability, failure handling, provenance, and human review controls to ensure AI applications are secure, reliable, and production-ready. • Enterprise Integration & Security: Integrate with enterprise and legacy systems while meeting requirements for authentication, auditability, data protection, PHI handling, and compliance. • Technical Leadership: Mentor junior engineers, participate in architecture and code reviews, establish engineering standards, and improve shared AI platform capabilities. • Business & Vendor Collaboration: Work directly with examiners, nurse reviewers, and operations teams to identify opportunities and translate business needs into engineering solutions. Partner with vendor engineering teams on shared development and delivery when needed. • Performance Optimization: Lead troubleshooting and root cause analysis for latency, cost, throughput, accuracy, and reliability issues across production AI services. Required Skills & Experience • Bachelor’s degree in Computer Science, Engineering, or a related quantitative discipline is required; master’s degree preferred. • 6–8 years of professional software engineering experience, including at least 3 years deploying and operating production GenAI agents, workflows, or applications. • Demonstrated ownership of production AI systems, including evaluation, failure handling, and human-in-the-loop review. Prototype- or demo-only experience does not qualify. • Hands-on experience building and operating production workloads on AWS, including cloud infrastructure and operational responsibilities. • Strong experience with Python, React, GitHub Actions, and Terraform, including ownership of infrastructure and CI/CD rather than simply modifying existing templates. • Strong full-stack engineering fundamentals, including REST APIs, SQL, service design, and enterprise system integration. • Experience with AI orchestration, tool use, evaluation, monitoring, and human review workflows. 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. • Experience co-developing solutions with business stakeholders or external vendor engineering teams.