About the Role
The Data Scientist will focus on optimizing and scaling AI/ML infrastructure, tools, and processes. This role involves analyzing performance bottlenecks, developing data pipelines, and implementing MLOps practices to improve the efficiency and reliability of ML workflows.
Responsibilities
- Analyze performance metrics of ML models and infrastructure to identify areas for improvement.
- Develop and maintain scalable data pipelines for training and inference.
- Implement MLOps best practices, including version control, CI/CD, and monitoring for ML systems.
- Design and conduct experiments to evaluate new tools and technologies for AI/ML infrastructure.
- Collaborate with ML engineers and researchers to optimize model training and deployment processes.
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, or a related quantitative field.
- 3+ years of experience in data science or MLOps, with a focus on ML infrastructure.
- Strong programming skills in Python and experience with cloud platforms (e.g., AWS, GCP, Azure).
- Experience with MLOps tools (e.g., Kubeflow, MLflow, Airflow).
- Familiarity with distributed systems and big data technologies.
Qualifications
- Ph.D. in a relevant field.
- Experience with GPU optimization and specialized hardware for ML.
- Contributions to open-source MLOps tools.