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# Senior Data & AI Engineer

- **Company:** Quantum Technologies
- **Location:** Tampa, FL, United States
- **Experience:** Expert
- **Salary:** $185,120.0
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Computer Programming, Continuous Integration, Information Engineering, Extract Transform Load (ETL), DevOps, Github, Python (Programming Language), Machine Learning, Tensorflow, Prometheus, Azure Machine Learning, Software Engineering, Data Logging, Google Cloud, Pytorch, Grafana, Git, Containerization, Scikit Learn, Kubernetes, Information Technology, Machine Learning Operations, Terraform, Software Version Control, Docker, Jenkins
- **Published:** October 27, 2026
- **Apply:** https://www.dice.com/job-detail/e313dd0a-4eca-49e7-8217-2fcf96ac8e97

## About the Role

* Strong programming skills in Python.
 * Experience with ML frameworks such as TensorFlow| PyTorch| or Scikit-learn.
 * Hands-on experience with CI/CD tools (Azure DevOps| Jenkins| GitHub Actions).
 * Knowledge of Docker| Kubernetes| and Infrastructure as Code (Terraform| ARM| etc.).
 * Experience with cloud platforms (Azure ML| AWS SageMaker| Google Cloud Platform Vertex AI).
 * Familiarity with version control systems such as Git.
 * Knowledge of monitoring and logging tools (Prometheus| Grafana| ELK| Azure Monitor).
 * Understanding of data engineering concepts and ETL pipelines.

Preferred Qualifications

 * Bachelors or Masters degree in Computer Science| Data Science| Engineering| or related field.
 * Relevant certifications in Azure| AWS| Kubernetes| or DevOps.
 * Experience implementing enterprise-scale AI/ML solutions.

## Description

The MLOps Engineer is responsible for building| automating| deploying| monitoring| and maintaining Machine Learning (ML) models and platforms in production environments. The role bridges Data Science| Software Engineering| and DevOps practices to ensure scalable| reliable| and secure ML solutions.
Key Responsibilities

 * Design| build| and maintain end-to-end ML pipelines for training| testing| deployment| and monitoring.
 * Automate CI/CD processes for machine learning models and data workflows.
 * Deploy and manage ML models on cloud platforms such as Azure| AWS| or Google Cloud Platform.
 * Implement model monitoring| performance tracking| drift detection| and retraining strategies.
 * Collaborate with Data Scientists| Data Engineers| and Application Development teams to operationalize ML solutions.
 * Manage containerization and orchestration using Docker and Kubernetes.
 * Ensure security| compliance| scalability| and reliability of ML platforms.
 * Optimize infrastructure utilization and cloud costs for ML workloads.
 * \Troubleshoot production issues and perform root cause analysis.
 * Maintain documentation| standards| and best practices for MLOps processes.