Responsibilities
- Build, maintain, and optimize end-to-end MLOps pipelines for machine learning workflows.
- Deploy, monitor, and scale machine learning models in production environments.
- Implement CI/CD pipelines for ML workflows and model lifecycle management.
- Manage and optimize ML infrastructure using Docker, Kubernetes, and cloud platforms.
- Collaborate closely with Data Scientists and Engineering teams to productionize ML models.
- Ensure reliability, monitoring, and performance of ML systems in production.
- Maintain best practices for model versioning, experiment tracking, and reproducibility.
Basic qualifications
- Senior-level experience in Machine Learning / MLOps engineering
- Strong programming skills in Python
- Hands-on experience with ML frameworks such as: TensorFlow PyTorch scikit-learn
- TensorFlow
- PyTorch
- scikit-learn
- Experience with MLOps platforms/tools such as: MLflow Kubeflow TFX or similar
- MLflow
- Kubeflow
- TFX or similar
- Experience implementing CI/CD pipelines using tools such as: Jenkins GitLab CI CircleCI
- Jenkins
- GitLab CI
- CircleCI
- Strong experience with containerization and orchestration: Docker Kubernetes
- Docker
- Kubernetes
- Experience deploying and managing ML solutions on cloud platforms (AWS, GCP, or Azure)
Preferred qualifications
- Experience with big data technologies such as: Apache Spark Hadoop Kafka
- Apache Spark
- Hadoop
- Kafka
- Experience with data visualization tools: Tableau Power BI
- Tableau
- Power BI
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Remote Machine Learning Data Science Mlops Ai