MLOps Engineer role

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.

Qualifications:

* Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.

* 5+ years of experience as a Machine Learning Engineer or in a similar role.

* 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.