MLOps Engineer

We’re looking for an experienced MLOps Engineer to help design, deploy, and scale machine learning infrastructure in a cloud-native environment. This role focuses on building reliable pipelines, automating workflows, and supporting production ML systems that drive real business impact.Responsibilities:Build and maintain scalable ML pipelines using AWS SageMaker and related servicesDesign and manage containerized environments using KubernetesDevelop and orchestrate workflows with Apache AirflowCollaborate with data scientists and engineers to productionize machine learning modelsImplement CI/CD pipelines for ML workflows and infrastructureMonitor, troubleshoot, and optimize model performance in productionEnsure reliability, scalability, and security of ML systemsRequirements:3+ years of experience in MLOps, DevOps, or related engineering rolesStrong experience with AWS, especially SageMakerHands-on experience with Kubernetes in production environmentsExperience building and managing workflows with Apache AirflowProficiency in Python and familiarity with ML frameworks (e.g., TensorFlow, PyTorch, or similar)Experience with infrastructure as code (Terraform or similar is a plus)Strong understanding of CI/CD pipelines and cloud architectureNice to Have:Experience with model monitoring and observability toolsBackground in data engineering or distributed systemsFamiliarity with feature stores and ML lifecycle management tools