MLOps Engineer

Tasks

• Design, build, and maintain end-to-end ML pipelines for training, testing, deployment, and monitoring.
• Automate CI/CD pipelines for ML models and data workflows.
• Deploy and manage ML models on Azure, AWS, or GCP.
• Implement model monitoring, performance tracking, drift detection, and automated retraining.
• Containerize applications using Docker and manage workloads with Kubernetes.
• Implement Infrastructure as Code using Terraform or ARM templates.
• Collaborate with Data Scientists, Data Engineers, and Application Development teams.
• Monitor production environments, troubleshoot issues, and perform root cause analysis.
• Optimize cloud infrastructure, security, scalability, and cost efficiency.

Requirements

• Strong Python programming skills
• ML frameworks: TensorFlow, PyTorch, or Scikit-learn
• CI/CD: Azure DevOps, Jenkins, or GitHub Actions
• Cloud ML platforms: Azure ML, AWS SageMaker, or GCP Vertex AI
• Docker and Kubernetes
• Terraform, ARM templates, and Infrastructure as Code
• Git and version control
• Monitoring tools: Prometheus, Grafana, ELK, or Azure Monitor
• Data engineering concepts and ETL pipelines
• Experience deploying and maintaining enterprise-scale AI/ML solutions

Location

• Tampa, FL