MLops Engineer

MLOps Engineer Duration- 3 Months Contract. Location: Dallas TX (onsite/Hybrid) About the Role We are seeking an MLOps Engineer to bridge the gap between data science and production systems, ensuring that machine learning models are deployed, monitored, and maintained at scale. You will work closely with data scientists, data engineers, and software developers to design and implement automated, reliable, and secure ML pipelines from development to production. Key Responsibilities • Model Deployment & Serving • Deploy ML models into production environments using tools such as Docker, Kubernetes, and model serving frameworks (e.g., TensorFlow Serving, TorchServe, MLflow). • Implement CI/CD pipelines for ML workflows. • Pipeline Development & Automation • Build and maintain end-to-end machine learning pipelines for data ingestion, preprocessing, training, validation, deployment, and monitoring. • Automate model retraining and versioning to ensure continuous improvement. • Monitoring & Maintenance • Set up monitoring and alerting systems for model performance, data drift, and infrastructure health. • Troubleshoot and resolve model degradation issues in production. • Collaboration & Integration • Collaborate with data scientists to transition models from experimentation to production-ready systems. • Work with DevOps and cloud teams to ensure ML workloads are scalable and cost-efficient. • Security & Compliance • Ensure compliance with data governance, security, and privacy regulations. • Manage role-based access control (RBAC) for ML infrastructure.