MLOps Engineer

The job overview for this position is not clearly labeled, but it can be inferred from the text. The first paragraph provides a brief introduction to PayPay, a fintech company with over 57 million users. The second paragraph invites candidates with experience in building ML models and MLOps to apply for a challenging and rewarding opportunity to work with a team of experts on cutting-edge technologies. This suggests that the job involves designing and deploying scalable and efficient ML models for various applications, developing and maintaining MLOps pipelines for data preparation, model training, and deployment, implementing strategies for model monitoring, evaluation, and maintenance, collaborating with Data Scientists, Engineers, and stakeholders to meet business requirements, exploring and integrating new ML techniques and tools, and documenting and communicating ML models and MLOps pipelines. The job requirements include strong experience in ML development and deployment using frameworks like TensorFlow, PyTorch or Scikit-learn, experience with MLOps tools and frameworks like Kubeflow, MLflow, or AWS SageMaker, strong Python programming skills and experience with software engineering best practices, experience with cloud platforms like AWS, GCP, or Azure, good understanding of Data Science, Data Engineering, DEVOps principles, excellent communication skills, and a Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field.

• Design, and deploy scalable and efficient ML models for various applications
• Develop and maintain MLOps pipelines for data preparation, model training, and deployment
• Implement strategies for model monitoring, evaluation, and maintenance
• Collaborate with Data Scientists, Engineers, and stakeholders to meet business requirements
• Explore and integrate new ML techniques and tools
• Document and communicate ML models and MLOps pipelines
• Knowledge about Microservices
• Knowledge about observability and how to gather data
• System design experience and capacity planning for large distributed systems
• Understanding of Automation tools and implementation
• Terraform/cloud formation experience
• Experience with managing monitoring tools like Cloudwatch, NewRelic, etc. Good understanding of DevOps concepts and implementation
• Strong experience in ML development and deployment using frameworks like TensorFlow, PyTorch or Scikit-learn
• Experience with MLOps tools and frameworks like Kubeflow, MLflow, or AWS SageMaker
• Strong Python programming skills and experience with software engineering best practices
• Experience with cloud platforms like AWS, GCP, or Azure
• Good understanding of Data Science, Data Engineering, DEVOps principles
• Excellent communication skills and ability to work in a team
• Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field

• Strong experience in ML development and deployment using frameworks like TensorFlow, PyTorch or Scikit-learn
• Experience with MLOps tools and frameworks like Kubeflow, MLflow, or AWS SageMaker
• Strong Python programming skills and experience with software engineering best practices
• Experience with cloud platforms like AWS, GCP, or Azure
• Good understanding of Data Science, Data Engineering, DEVOps principles
• Excellent communication skills and ability to work in a team
• Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field