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# AI Platform Engineer

- **Company:** Advanced
- **Location:** Naples, FL, United States
- **Experience:** Experienced
- **Salary:** $140,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Computer Programming, Databases, Continuous Delivery, Continuous Integration, DevOps, Monitoring of Systems, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Feature Store, Pytorch, Apache Spark, Containerization, AI Platforms, Scikit Learn, Kubernetes, Information Technology, Apache Kafka, Data Management, Machine Learning Operations, Drift Detection, Api Design, Data Pipelines, Docker
- **Published:** November 2, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-platform-engineer-naples-fl--caa07d6c-c6c9-4105-97c9-4e2ed0d1e422

## About the Role

Artificial Intelligence (AI), Data Science, Production Systems, Machine Learning, Artificial Intelligence (AI), Equipment Maintenance/Repair, Continuous Deployment/Delivery, Continuous Integration, Data Management, Performance Tuning/Optimization, * Bachelor's or Master's degree in Computer Science, Engineering, or related field

 * 4+ years experience in DevOps/Infrastructure with 2+ years focused on ML systems
 * Proficiency in container technologies (Docker, Kubernetes) and cloud platforms
 * Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and MLOps tools
 * Strong programming skills in Python, with knowledge of infrastructure-as-code
 * Experience with data pipeline tools (Airflow, Kafka, Spark) and databases
 * Understanding of ML model serving frameworks and API development
 * Knowledge of monitoring tools and observability practices for ML systems
*

## Description

Build and scale the infrastructure that powers AI at enterprise scale. Design robust, automated systems that enable data scientists and ML engineers to deploy, monitor, and maintain machine learning models in production environments., * Design and implement MLOps pipelines for model training, deployment, and monitoring

 * Build automated CI/CD systems for machine learning model lifecycle management
 * Develop infrastructure for real-time and batch ML inference at scale
 * Implement model monitoring, drift detection, and automated retraining systems
 * Design data pipelines and feature stores for ML model development and serving
 * Collaborate with data science teams to productionize research models
 * Optimize ML infrastructure for performance, cost, and reliability
 * Implement security and compliance controls for ML systems and data