Job Summary

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

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

Skill Requirements

• Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, and scikit-learn. • Strong understanding of MLOps best practices and tools, including Kubeflow, Seldon, MLFlow, Docker, and Kubernetes. • Experience working with cloud platforms, especially GCP. • Knowledge of data processing, ETL, and feature engineering techniques. • Strong problem-solving skills and ability to work in a fast-paced, collaborative environment. • Excellent communication and interpersonal skills.

Other 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).