AI/ML Platform Engineer

Motion Recruitment; Nashville (TN); Remote; USD 140,000 - 180,000; Full time

Job summary

Motion Recruitment is seeking an AI/ML Platform Engineer for a global leader in claims management. This contract-to-hire role is 100% remote with CST working hours, offering strong growth potential within a $21 billion organization.

You will build and deploy production-ready AI agents and platforms under a Principal AI Engineer, collaborating with end users to solve real business problems while scaling enterprise AI capabilities.

Qualifications

• 5+ years in DevOps, platform engineering, or MLOps.
• Hands-on with Azure, AWS, or GCP.
• Experience with Docker, Kubernetes, and cloud-native deployments.
• Proven CI/CD design and automated release management.
• Infra-as-code using Terraform, Bicep, or CloudFormation.
• Strong scripting in Python, PowerShell, or Bash.
• Experience with monitoring, observability, and security practices.

Responsibilities

• Build and maintain a shared AI platform for development and deployment.
• Design CI/CD pipelines for AI apps, APIs, containers, models, and agents.
• Establish deployment, environment, and release-management patterns.
• Implement monitoring, logging, tracing, alerting, and observability.
• Ensure platform security, secrets management, and access controls.
• Support production operations and reliability across AI projects.

Skills

DevOps & Platform

CI/CD pipelines

Cloud platforms

MLOps / LLMOps

SRE practices

Automation scripting

Security & IAM

Containerization

Observability

Incident management

Tools

Docker

Kubernetes

Terraform

Bicep

CloudFormation

Azure DevOps

GitHub Actions

Jenkins

Python

Job description

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.