Machine Learning Engineer

A large financial organization is seeking a Machine Learning Engineer that can sit fully remote or hybrid onsite in Cincinnati Ohio for a long term contract with the possibility of full time hire. The ideal candidate will have a strong background in machine learning, data science, and cloud computing, with specific experience in deploying and managing models using AWS SageMaker.

JOB DESCRIPTION
Join our Data Science Enablement squad as a Senior Machine Learning Engineer. You will use an existing batch inference model to establish a secure, automated deployment pipeline. This role involves both engineering and change management, including architecture and training, with a focus on educating data scientists and other Data Science Enablement members on MLOps. Once the foundational deployment framework is in place, you will enable additional MLOps capabilities such as MLFlow, A/B testing, real-time endpoints, and further 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.

Squad outcomes:
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)
o Currently, data scientist build model features (attributes) about customers in their own Jupyter notebook that feed into their models and never reusable 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 its batch and can be almost 31 days behind.
o Technology used: Docker, Kafka, Snowflake, Feature Store

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com .

To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/ .

Required Skills & 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.
Proficiency in programming languages such as Python, R, SQL and/or Java.
Use of standard DevOps tools such as Jira, Terraform, GitHub, Jenkins
Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).

Nice to Have Skills & Experience

Experience in highly regulated industries like banking, finance, or healthcare.
DBT
Snowflake

Benefit packages for this role will start on the 31st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.