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Known services firm, popular ML-Ops role with broad toolset, likely competitive candidate density.
MLOps and cloud skills transfer across industries, though enterprise AWS/SageMaker emphasis raises domain specificity.
Explicit 7+ years requirement and multiple mandatory MLOps/AWS tools increases filter strictness.
Build, deploy, and manage end-to-end machine learning lifecycle pipelines including automation of training, testing, deployment, and monitoring.
Implement and maintain model versioning, experiment tracking, and model registry to ensure operational governance and auditability.
Collaborate across Data Engineering and DevOps teams to operationalize ML solutions with a focus on performance monitoring and security controls.
4+ years relevant experience as MLOps Engineer, 7+ years total industrial experience.
Strong Python programming skills with hands-on experience in MLflow, Kubeflow, Amazon SageMaker, AWS Step functions, and Amazon ECS.
Experience with Docker, Kubernetes, CI/CD tools, DevSecOps practices, and Infrastructure-as-Code tools like Terraform or CloudFormation.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
Experienced in taking ML/AI solutions from Proof of Concept to Production in enterprise cloud environments, preferably AWS.
Skilled at integrating ML lifecycle management tools and automation platforms to optimize production model monitoring and governance.
Familiar with advanced topics such as Responsible AI, AI governance, generative AI, LLMOps, and data engineering tools (e.g., Databricks, Spark).