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 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 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).
• Experience with snowflake and cloud (AWS, GCP, Azure, Oracle).