Our client, a global leader in claims management and the largest third-party administrator (TPA) in the world, is actively looking for an AI/ML Platform Engineer to join their team! This is a 100% remote opportunity however working hours will be 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 under a Principal AI Engineer, you’ll build and deploy production-ready AI agents, workflows, and applications while collaborating directly with end users to solve real business problems.

Responsibilities

Build and maintain the shared platform used to develop, deploy, and operate AI applications. Design CI/CD pipelines for AI applications, APIs, containers, models, and agents. Establish standardized deployment, environment, configuration, and release-management patterns. Implement monitoring, logging, tracing, alerting, and AI observability. Build and operate automated AI evaluation and quality-monitoring pipelines. Monitor model usage, token consumption, infrastructure cost, latency, and performance. Manage container, dependency, infrastructure, and application vulnerability remediation. Implement platform security, secrets management, access controls, and operational standards. Support production operations and reliability across all AI projects. Required Skills & Experience 5+ years of DevOps, Cloud Engineering, Platform Engineering, MLOps, SRE, or related experience. Strong hands-on experience with Azure, AWS, or GCP cloud platforms. Experience with Docker, Kubernetes or equivalent container orchestration, and cloud-native deployments. Strong experience designing and managing CI/CD pipelines and automated release processes. Experience with infrastructure-as-code using Terraform, Bicep, CloudFormation, or similar technologies. Strong scripting and automation skills using Python, PowerShell, Bash, or equivalent. Experience implementing application and infrastructure monitoring, logging, tracing, alerting, and production observability. Strong understanding of secrets management, configuration management, identity/access controls, and platform security. Experience with vulnerability scanning, container security, dependency management, patching, and remediation processes. Understanding of production troubleshooting, incident management, performance optimization, and application reliability. Knowledge of MLOps/LLMOps, AI gateways, AI evaluation pipelines, LLM observability, model/prompt versioning, and AI cost management is preferred. Experience with Azure DevOps, GitHub Actions, Jenkins, or similar engineering platforms is a plus. Ability to build reusable platform capabilities and operational standards supporting multiple AI development teams.