Sedona Digital · Serbia
Skills: Python, AWS, Docker, CI/CD
Join Sedona Digital, a fast-growing scale-up organization with an ambition to be recognized as one of the leading technology companies servicing high-tech, global enterprises across Technology, Finance, and Life Sciences sectors. Our global client base needs builders: engineers and developers who love technology, have deep expertise in software, engineering, and cloud technologies, and importantly, have a passion for culture and customers.
We obsess about our customers We build outstanding technical solutions We create an awesome culture We accelerate learning and careers
We are looking for a skilled MLOps Engineer to join our team and play a key role in bridging the gap between our Data Science and Infrastructure teams. You will be responsible for supporting the Data Science team in MLOps-related tasks while also helping in DevOps initiatives, including CI/CD pipeline creation, provisioning of cloud resources using tools like Terraform, and Kubernetes orchestration. You will collaborate closely with data scientists and engineers to deploy data pipelines, train machine learning models, and manage their deployment within scalable cloud environments while ensuring high performance, security, and reliability throughout the ML lifecycle.
The main responsibilities of the position include:
Assist in designing, implementing, and maintaining scalable MLOps pipelines on AWS using services such as SageMaker, EC2, EKS, S3, Lambda and other relevant AWS tools Coordinate with our platform team to troubleshoot Kubernetes clusters (EKS) to orchestrate the deployment of machine learning models and other microservices Develop and maintain CI/CD pipelines for model and application deployment, testing, and monitoring Collaborate closely with Data Science, and DevOps team to streamline the model development lifecycle, from experimentation to production deployment Implement security best practices, including network security, data encryption, and role-based access controls within the AWS in…
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