Description:

The Senior MLOps Engineer will build and maintain MLOps pipelines for seamless development, deployment, and monitoring of ML models.
This role involves automating model training, evaluation, and deployment processes.
The engineer will monitor model performance in real-time and implement solutions for drift detection.
Responsibilities include designing and managing cloud infrastructure to support ML model deployment and scalability.
The position requires optimizing resource usage and cost-efficiency on cloud platforms such as AWS, GCP, and Azure.
The engineer will also automate CI/CD pipelines for both backend and frontend systems.

Requirements:

Candidates must have strong experience with MLOps tools and platforms, including MLflow, Kubeflow, and SageMaker.
Expertise in cloud platforms such as AWS, GCP, and Azure, along with infrastructure-as-code tools like Terraform and CloudFormation, is required.
Proficiency in scripting and automation using languages such as Python, Bash, and Ansible is necessary.
Knowledge of container orchestration and deployment tools, including Kubernetes and Docker, is essential.
Familiarity with monitoring and logging tools, such as Prometheus and Grafana, is expected.
Experience with version control and CI/CD practices is required.
A strong understanding of the ML model lifecycle and deployment challenges is essential for this role.

The position offers the opportunity to work with top-notch organizations and talents globally.
Employees will have the chance to contribute to innovative MLOps solutions and practices.
The role provides a dynamic work environment that encourages professional growth and development.
Competitive compensation and benefits package will be offered to the successful candidate.