Description

You will own the end-to-end lifecycle of machine learning solutions.

Responsibilities

• Build, test, and deploy machine learning and deep learning models according to defined MLOps strategies.
• Design and maintain production-grade machine learning pipelines, integrating them with operational processes.
• Process and transform source data for ML pipelines, utilizing cloud platforms for scalability.
• Collaborate cross-functionally to operationalize AI technologies for complex business problems.
• Document models and code thoroughly, supporting model governance and compliance.

Required Skills

• 5+ years experience in machine learning and data science, focused on model operationalization and MLOps workflows.
• 5+ years hands-on experience with Python, classical ML, and deep learning frameworks (Scikit-learn, PyTorch, TensorFlow).
• 5+ years leading MLOps projects, demonstrating technical leadership and strong communication.
• Proven production ML delivery experience (MLOps, CI/CD).
• Cloud-native deployment experience (Azure/Databricks preferable).
• Expertise in Python and PySpark.
• Familiarity with version control using GitHub.
• Experience with MLflow or Optuna for experimentation tracking.

Key Skills

Education

Bachelor's degree