Machine Learning Operations (Entry Level)

Start Date: July 2025

The MLOps Engineer on the Enterprise Data and Analytics Team will work as part of the larger team to collaborate with Data Scientists to productionize Models used by business users to enhance their ability to make better decisions for the company.

Primary Responsibilities:

Understand the current process and technical complexities of developing and deploying data pipelines and model builds and develop automation solutions to improve and extend the existing process to become an unattended delivery pipeline.

Collaborate closely with product development, architecture, data engineering and testing teams to understand their current build and release processes and make recommendations for improvement through the automation of various tasks.

Partner with cross-functional stakeholders, including development, operations, quality assurance and security, to streamline processes.

Develop and continuously improve automation solutions to enable teams to build and deploy quality data and code efficiently and consistently.

Build automated testing solutions in support of quality management objectives to reduce manual effort.

Debug and troubleshoot machine learning model issues during production to ensure performance and stability.

Work closely with cross-functional stakeholders to analyze and troubleshoot complex production issues.

Prepare and present design and implementation documentation to multiple stakeholders.

Promote automation across the data management and analytics delivery organization.

Schedule and facilitate meetings as needed.

Perform other duties as assigned.

Minimum Requirements:

Bachelor's Degree in Computer Science, Machine Learning, or Related Field.

Experience with one or more coding languages such as Python, C++, or Java.

Experience with or understanding of CI/CD pipelines using containerization tools such as Docker.

Experience with automation using platforms such as GitLab CI, Jenkins, etc.

Experience with or exposure to basic cloud storage services (e.g., S3, Google Cloud Storage), compute, (e.g., EC2, Lambda, etc.) or managed machine learning services (e.g., AWS SageMaker, GCP AI Platform).

Experience with OpenAPI or Swagger.

Experience with Version Control tools such as Git.

Brooksource provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, national origin, age, sex, citizenship, disability, genetic information, gender, sexual orientation, gender identity, marital status, amnesty or status as a covered veteran in accordance with applicable federal, state, and local laws.