About the job
Job Purpose
Provides engineering support to the ML Platform team by building common components and assisting in the automation of model deployment workflows. Facilitates the migration of domain-specific data science projects from local environments to cloud-based, production-ready infrastructures.
Key Result Key Responsibilities
Assists in the development and maintenance of internal Python libraries for feature engineering and model evaluation.
Supports the creation of Docker images and the deployment of services to Kubernetes or Snowpark Container Services.
Monitors production pipelines in Airflow and assists in troubleshooting job failures or latency issues.
Key Result Key Responsibilities-Continued
Helps implement basic unit tests and pre-commit hooks (e.g., Black, Flake8) to ensure code quality across repositories.
Updates and maintains platform documentation, user guides for the data science community.
Qualifications (Academic, training, languages)
Bachelor's degree in Computer Science, Information Technology, or a similar technical field.
Fluent in English Language.
Deep expertise in machine learning, statistics, and applied modeling.
Proficient in MS Office applications.
Proficient in Python (OOP, modular design) and SQL.
Work Experience With 2-4 years of professional experience in data science, data engineering, or a junior MLOps role.
Experience designing scalable and impactful solutions.
Experience with Kubernetes, Docker, and Infrastructure as Code (Terraform).