Czym będziesz się zajmować? Your key responsibilities: Design, build, and maintain end-to-end ML pipelines using SageMaker Pipelines or Kubeflow. Implement CI/CD workflows for ML using tools such as GitHub Actions, GitLab, Jenkins, or AWS CodePipeline. Manage containerized deployments with Docker and Kubernetes (ECS/EKS). Oversee model versioning, registry, and tracking with MLflow or SageMaker Model Registry. Develop and operate monitoring solutions for model performance, drift detection, and data quality. Build and maintain data engineering workflows using AWS Glue, EMR, Spark, or PySpark. Implement Infrastructure as Code (IaC) using Terraform or AWS CloudFormation. Ensure observability and reliability using CloudWatch, Prometheus, ELK, or Datadog. Follow AWS security best practices (IAM, VPC, KMS, Secrets Manager, PrivateLink). Collaborate with cross-functional teams to integrate ML systems into production applications. 

Kogo poszukujemy? We are looking for an experienced MLOps Engineer to design, build, and optimize scalable machine learning infrastructure on AWS. In this role, you’ll work closely with data scientists and software engineers to productionize ML models, automate workflows, and ensure reliability, security, and observability across our ML ecosystem. You’ll be responsible for shaping and maintaining the full ML lifecycle — from data pipelines and model training to deployment, monitoring, and continuous improvement. Design, build, and maintain end-to-end ML pipelines using SageMaker Pipelines or Kubeflow. Implement CI/CD workflows for ML using tools such as GitHub Actions, GitLab, Jenkins, or AWS CodePipeline. Manage containerized deployments with Docker and Kubernetes (ECS/EKS). Oversee model versioning, registry, and tracking with MLflow or SageMaker Model Registry. Develop and operate monitoring solutions for model performance, drift detection, and data quality. Build and maintain data engineering workflows using AWS Glue, EMR, Spark, or PySpark. Implement Infrastructure as Code (IaC) using Terraform or AWS CloudFormation. Ensure observability and reliability using CloudWatch, Prometheus, ELK, or Datadog. Follow AWS security best practices (IAM, VPC, KMS, Secrets Manager, PrivateLink).