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In Cyclad we work with top international IT companies in order to boost their potential in delivering outstanding, cutting-edge technologies that shape the world of the future. We are seeking a highly experienced Senior Machine Learning Engineer to join a remote development team. The role focuses on designing, deploying, and maintaining scalable machine learning solutions in production environments, with a strong emphasis on MLOps practices and ML lifecycle management using MLflow. The candidate will collaborate closely with data scientists, data engineers, architects, and business stakeholders to deliver reliable, production-grade ML systems.
Job Responsibility:
Design, develop,and deploy machine learning models for production environments
Implement and manage MLflow for experiment tracking, model versioning, and model deployment
Optimize ML pipelines for performance, scalability, reliability, and cost efficiency
Implement end-to-end ML pipelines covering training, validation, deployment, monitoring, and retraining
Apply MLOps best practices to ensure scalability, reproducibility, and operational stability
Collaborate with data scientists to operationalize models and improve model performance
Work closely with data engineers to integrate ML solutions with data pipelines and data platforms
Build and maintain containerized ML applications using Docker and Kubernetes
Implement CI/CD pipelines for automated testing, deployment, and monitoring of ML models
Monitor model performance, data drift, and system health
perform root cause analysis when issues arise
Ensure security, compliance,and best practices across cloud-based ML deployments
Requirements:
Minimum 8+ years' experience in machine learning engineering or related roles
Strong proficiency in Python and modern machine learning frameworks (TensorFlow, PyTorch, Scikit-learn)
Hands-on experience with MLflow for model lifecycle management in production environments
Solid understanding of MLOps principles, including CI/CD, containerization, and orchestration
Experience deploying and operating ML workloads on cloud platforms (AWS, Azure, or GCP)
Good knowledge of data engineering concepts and tools such as Spark and Airflow
Strong problem-solving skills and ability to work in complex, distributed environments
Excellent verbal and written communication skills in English
What we offer:
Private medical care with dental care (covering 70% of costs) + rehabilitation package. Family package option possible