Assist in designing and implementing ML pipelines and services in cloud and on-premise environments.
Collaborate with cross-functional teams to gather requirements and contribute to scalable ML solutions.
Support model deployment, monitoring, and performance optimization.
Apply MLOps practices such as CI/CD, model versioning, and retraining workflows.
Work with Snowflake and AWS services to support ML model deployment.
Contribute to the development and maintenance of on-premise ML workflows.
Ensure adherence to data privacy, security, and compliance standards.
Participate in code reviews and knowledge sharing within the team.
Requirements
Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
2–4 years of experience in machine learning engineering or related roles.
Proficiency in Python, along with data processing and ML libraries (e.g., Pandas, NumPy, scikit-learn, XGBoost).
Familiarity with AWS and/or Snowflake cloud services, as well as Linux-based systems.
Basic experience with Jenkins and containerization tools such as Docker.
Strong SQL skills, including an understanding of query optimization techniques.
Exposure to ML workflow tools (e.g., MLflow, Airflow) and monitoring frameworks is a plus.
Strong analytical and problem-solving abilities.
Benefits
Compelling rewards package including base compensation, eligibility for annual bonus, retirement savings, share plan, health benefits, personal wellness, and volunteer opportunities.
Hybrid flexible work model.
Outstanding career development opportunities.
We’ll support your professional development education.
Competitive vacation package with the option to purchase 5 extra days off per year.
Employee-driven programs focused on gender, LGBTQ+, origins, diversity, and inclusion.
Corporate wellness programs to support our employees’ physical and mental health.
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