<p>Software and Data Engineering: Utilize your expertise in Java and Kubernetes to build and maintain robust data solutions. DevOps and monitoring solutions as well as a strong knowledge of the SDLC are required. Build and manage dozens of data pipelines to source and transform data based on business requirements. MLOps Engineering: Implement and manage machine learning models in production environments. Ensure the reliability and scalability of ML workflows using tools like MLflow, Kubeflow, and AWS SageMaker. Innovation and Learning: Quickly learn new technologies by applying your current skills, staying ahead of industry trends and advancements. Self-identify the need for new skills to be developed and adopt new technologies into your skill set in a month's time. 5+ years of experience in AWS services such as S3, EC2, Lambda, RDS, and Redshift. 5+ years of experience with advanced skills in Python for data processing, automation, and scripting. 5+ years of experience with relational databases and SQL for querying and managing data. 3+ years of experience with MLflow, Kubeflow, and AWS SageMaker for managing ML models in production. Data Integration: Ability to integrate data from various sources using AWS Glue or similar ETL tools. 3+ years of experience in building and maintaining scalable data pipelines using AWS services. 3+ years of experience in using Terraform or CloudFormation for infrastructure as code. 3+ years of experience in setting up monitoring and logging for data pipelines and AWS resources using tools like CloudWatch and ELK stack.</p>