Machine Learning Ops

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Requirements:

• 3+ years of experience in machine learning operations, model deployment, or related experience.
• Expertise in programming languages such as Python or R.
• Familiarity with ML frameworks (e.g. TensorFlow, PyTorch, Scikit-learn).
• Experience in cloud technologies like AWS, Google Cloud, or Azure.
• Strong skills in version control (e.g. Git) and CI/CD processes.
• Solid understanding of containerization technologies such as Docker and orchestration systems like Kubernetes.

Responsibilities:

• Design, develop, and implement scalable pipelines for data and model deployment in production environments.
• Monitor and maintain the performance of deployed models, ensuring they meet specified accuracy and robustness standards.
• Automate ML Workflow processes for continuous integration and continuous delivery (CI/CD) related to ML models.
• Collaborate with data scientists to transition models from development to production
• Ensure compliance with data privacy regulations and best practices in machine learning operations.
• Develop tools for monitoring and visualizing model performance and data drift.
• Conduct regular audits of ML systems to identify improvement opportunities.
• Train team members on best practices for deploying and maintaining machine learning models.