Darum lohnt es sich
Key Responsibilities
• Design, implement, and manage end-to-end MLOps pipelines for machine learning models.
• Automate model training, validation, deployment, and monitoring processes.
• Develop and maintain infrastructure for ML experimentation and production environments, primarily in cloud settings.
• Implement strategies for model versioning, drift detection, and performance tracking.
• Collaborate with data science and engineering teams to ensure seamless integration and operational readiness of ML solutions.
Benefits
• Highly competitive salary and performance bonuses.
• Comprehensive health, dental, and vision insurance.
• Full remote work flexibility with flexible working hours.
• Opportunities for professional development, certifications, and attending industry conferences.
• Contribute to cutting-edge AI projects from the comfort of your home office.
About the Role
Our client is seeking a dedicated Remote Machine Learning Operations (MLOps) Engineer to streamline and automate the lifecycle of their machine learning models.
In this fully remote position, you will be instrumental in building robust pipelines for model training, deployment, monitoring, and management, ensuring that our AI solutions are deployed efficiently and reliably.
You will collaborate closely with data scientists and software engineers to bridge the gap between model development and production, fostering a culture of operational excellence in AI.
We are looking for an experienced MLOps professional who can design, implement, and maintain the systems that support our rapidly growing AI initiatives, operating remotely to serve our global client base.
Requirements
• Bachelor's degree in Computer Science, Engineering, or a related field.
• 2+ years of experience in MLOps, DevOps, or a related role with a focus on machine learning systems.
• Proficiency with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Strong understanding of CI/CD principles, scripting languages (Python, Bash), and infrastructure-as-code tools.
