MLOps Engineer

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 infrastructure

Monitor, troubleshoot, and optimize data and ML pipelines to ensure high availability and performance

Set up and manage model monitoring systems for performance drift, ensuring continuous model improvement

Bachelor’s degree in Computer Science, Engineering, or related field

2+ years of hands-on experience in MLOps, DevOps, or related fields

Knowledge and preferable working experience in AWS services for machine learning, such as SageMaker, EKS, S3, EC2, Lambda, and others

Exposure to Kubernetes for container orchestration

Experience with Docker

Exposure to infrastructure-as-code tools such as Terraform or CloudFormation

Familiarity with CI/CD tools such as GitLab CI

Understanding machine learning model lifecycle

Familiarity with monitoring and logging solutions like Prometheus, Grafana, CloudWatch and ELK Stack