Machine Learning Engineer

Job Title: ML Ops EngineerLocation: Downtown CincinnatiIndustry: Banking & Financial ServicesYears of Experience: 3+ yearsTOP SKILLS:AWS SagemakerPythonSQLMLOpsWhat You'll DoJoin our Data Science Enablement squad as a Senior Machine Learning Engineer. You willuse an existing batch inference model to establish a secure, automated deployment pipeline.This role involves both engineering and change management, including architecture andtraining, with a focus on educating data scientists and other Data Science Enablementmembers on MLOps. Once the foundational deployment framework is in place, you willenable additional MLOps capabilities such as MLFlow, A/B testing, real-time endpoints, andfurther automation with Model Risk Management (MRM).Key Responsibilities:Develop and implement a secure, automated deployment pipeline.Educate and mentor team members on MLOps practices.Balance engineering tasks with change management and training.Enhance MLOps capabilities with advanced tools and techniques.Preferred Experience:• Experience in highly regulated industries like banking, finance, or healthcare.Qualifications:Experience:Minimum of 3-5+ years of experience in machine learning and MLOps.Proven experience with AWS Sagemaker and building end-to-end machine learning models.Experience with data integration and management using IBM DB2 and Snowflake (or like databases)Strong understanding of CI/CD pipelines and automation tools. TechnicalSkills:Proficiency in programming languages such as Python, R, SQL and/or Java.Use of Fifth Third standard DevOps tools such as Jira, Terraform, GitHub, JenkinsKnowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).