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# Machine Learning Engineer

- **Company:** Strategic Inc
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, JIRA, Cloud Computing, Databases, Continuous Integration, Data Integration, IBM DB2, DevOps, Github, R (Programming Language), Python (Programming Language), Machine Learning, SQL Databases, Jupyter Notebook, Feature Store, Snowflake, Containerization, Kubernetes, Apache Kafka, Machine Learning Operations, Model Registry, Terraform, Docker, Jenkins
- **Published:** October 7, 2026
- **Apply:** https://www.thejobnetwork.com/job/b38a82a4-4d67-4a14-95c9-cafb85da107f/senior-mlops-engineer

## About the Role

* Experience in highly regulated industries like banking, finance, or healthcare., * 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.
 * Technical Skills:
 * Proficiency in programming languages such as Python, R, SQL and/or Java.
 * Use of Fifth Third standard DevOps tools such as Jira, Terraform, GitHub, Jenkins
 * Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).

## Description

* 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., * Future (2025 & Beyond) - Utilize AWS Sagemaker to expand Feature Store, introduce Model Registry, CI/CD, Real-Time models for our large data science credit models.
 * The squad is currently working on an in-house build of Feature Store to help speed up modeling process for our Data Science department. Combination of Snowflake, Cloud Pak for Data. (More on this later)
 * Currently, data scientist build model features (attributes) about customers in their own Jupyter notebook that feed into their models and never reuseable for others… aka reason for Feature Store
 * They are also working on building real time scoring framework for our loan/card application process. Right now it's batch and can be almost 31 days behind.
 * Technology used: Docker, Kafka, Snowflake, Feature Store