Description:
jd
position: mlops engineer
the ml / mlops engineering team is responsible for the successful implementation and iteration of ai/ml solutions including providing architectural guidance and development support. Some of our focus areas include: developing reusable frameworks, code optimization and refactoring, scaling up ml solutions, and foreseeing and testing for common issues that may arise in production. We are looking for a highly capable senior ml or mlops engineer with a strong software engineering and devops background. As a senior mlops engineer, you will be embedded and supporting a revenue generation or cost optimization project, ensuring its success in production by improving the code, creating automated ci/cd testing, and developing frameworks that can be reused for other similar projects.
activities:
build, maintain, and document machine learning frameworks (python packages) used across multiple projects.
· support a project team with data scientists, business stakeholders, analysts, and data engineers.
· develop reusable feature stores for rules-based and ai/ml models.
· implement monitoring capabilities for model performance and effectiveness in production.
· automate ci/cd testing and deployments incorporating mlops best practices.
skills:
-bachelor's degree in software engineering, computer science, data science, mathematics, or a related field.
· 5+ years of overall experience in data analytics.
· 3+ years of experience with ml engineering and/or ml ops. Up to 2 years of software engineering or data engineering experience can also count towards this requirement.
· sharp critical thinking skills and ability to learn and question complex processes and solutions.
· experience building scalable machine learning systems and data-driven products working with cross-functional teams.
· experience creating python packages
· well-developed software engineering skills, including use of proper development, qa, and production environments, object-oriented programming, version control, and knowledge of multiple programming languages.
· proficiency in python and experience with common data analytics packages (e.g. Numpy, pandas, sklearn, pyspark).
· proficiency in sql and azure
· good communication skills and the ability to understand and synthesize requirements across multiple project domains.