JOB DESCRIPTION We have an exciting and rewarding opportunity for you to take your software engineering career to the next level As a Software Engineer III at JPMorgan Chase within the Employee Platforms team you serve as a seasoned Data Engineer with solid experience in AWS Python and MLOps to design build and maintain scalable data and machine learning infrastructure This role will collaborate with data scientists and software engineers to enable efficient data processing model deployment and monitoring in cloud environments Job responsibilities Design implement and optimize ETL ELT pipelines using Python and AWS services e g Glue Lambda S3 Redshift Support the deployment monitoring and maintenance of machine learning models in production leveraging MLOps best practices and tools Build and manage scalable data architectures on AWS ensuring reliability security and cost-effectiveness Collaborate closely with data scientists ML engineers and business stakeholders to understand requirements and deliver robust solutions Develop automated workflows for data ingestion transformation and model deployment using CI CD pipelines Monitor data pipelines and ML models for performance data drift and system health implement improvements as needed Document data processes architectures and model workflows ensure compliance with internal and regulatory standards Optimize data workflows and architectures for efficiency and scalability Integrate new data sources and technologies into existing data infrastructure Troubleshoot and resolve issues in data pipelines and machine learning operations Ensure adherence to best practices in data engineering security and compliance Required qualifications capabilities and skills Formal training or certification on software engineering concepts and 3 years applied experience Hands-on experience as a Data Engineer MLOps Engineer or in a closely related field Program proficiently in Python Utilize AWS cloud services e g S3 EC2 Lambda Glue Redshift SageMaker in data engineering tasks Apply MLOps tools and frameworks e g MLflow Kubeflow Airflow Docker Kubernetes in production environments Build and optimize data pipelines and workflows for large-scale data processing Implement CI CD concepts and tools e g Jenkins GitLab CI for automated deployments Model data effectively and apply warehousing and big data concepts Solve complex problems and communicate technical solutions clearly Adapt quickly to new technologies and evolving project requirements Preferred qualifications capabilities and skills Leverage infrastructure-as-code tools Terraform CloudFormation for cloud resource management Apply machine learning frameworks TensorFlow PyTorch Scikit-learn in model development and deployment Utilize data engineering concepts and tools Spark Kafka etc for advanced data processing Implement model governance and explainability frameworks in ML workflows ABOUT US