Job Summary

Job Summary : • Design, implement, and optimize MLOps pipelines using tools such as Kubeflow, Seldon, MLFlow, Docker, and Kubernetes

Job Description : • Develop and deploy end-to-end microservices-based solutions for batch and real-time algorithms, including monitoring, logging, automated testing, and performance testing.\\\\r\\\\n• Design, implement, and optimize MLOps pipelines using tools such as Kubeflow, Seldon, MLFlow, Docker, and Kubernetes.\\\\r\\\\n• Collaborate with Data Scientists to enhance the ML model development process and ensure performance improvements.\\\\r\\\\n• Ensure scalability, maintainability, and robustness of deployed machine learning models.\\\\r\\\\n• Monitor and troubleshoot ML model performance and infrastructure issues in production (experience with Prometheus and Grafana is valuable).\\\\r\\\\n• Support and enhance ML software infrastructure, including CI/CD, data storage, cloud services, security, and system monitoring.\\\\r\\\\n• Work with cloud platforms, particularly GCP and Azure, to optimize resource allocation and costs.\\\\r\\\\n• Stay up to date with the latest trends and best practices in MLOps.\\\\r\\\\n

Key Responsibilities

Job Responsibilities : • Develop and deploy end-to-end microservices-based solutions for batch and real-time algorithms, including monitoring, logging, automated testing, and performance testing. • Design, implement, and optimize MLOps pipelines using tools such as Kubeflow, Seldon, MLFlow, Docker, and Kubernetes. • Collaborate with Data Scientists to enhance the ML model development process and ensure performance improvements. • Ensure scalability, maintainability, and robustness of deployed machine learning models. • Monitor and troubleshoot ML model performance and infrastructure issues in production (experience with Prometheus and Grafana is valuable). • Support and enhance ML software infrastructure, including CI/CD, data storage, cloud services, security, and system monitoring. • Work with cloud platforms, particularly GCP and Azure, to optimize resource allocation and costs. • Stay up to date with the latest trends and best practices in MLOps

Skill Requirements

• Experience deploying ML models at scale using serverless or cloud-based solutions. • Familiarity with data visualization tools (Matplotlib, Seaborn, Plotly). • Knowledge of software development best practices (Git, CI/CD, automated testing).

Other Requirements

• Develop and deploy end-to-end microservices-based solutions for batch and real-time algorithms, including monitoring, logging, automated testing, and performance testing. • Design, implement, and optimize MLOps pipelines using tools such as Kubeflow, Seldon, MLFlow, Docker, and Kubernetes. • Collaborate with Data Scientists to enhance the ML model development process and ensure performance improvements. • Ensure scalability, maintainability, and robustness of deployed machine learning models. • Monitor and troubleshoot ML model performance and infrastructure issues in production (experience with Prometheus and Grafana is valuable). • Support and enhance ML software infrastructure, including CI/CD, data storage, cloud services, security, and system monitoring. • Work with cloud platforms, particularly GCP and Azure, to optimize resource allocation and costs. • Stay up to date with the latest trends and best practices in MLOps.