Description

You will own the development and deployment of machine learning solutions.

Responsibilities

• Conduct exploratory data analysis using scientific libraries and statistical methods to derive data insights.
• Build, train, test, validate, and productize machine learning models for high-performance applications.
• Implement MLOps best practices, including model versioning, monitoring, and CI/CD pipelines for models.
• Utilize AWS SageMaker to build, train, tune, and deploy machine learning models.
• Debug and analyze false positive and false negative cases while supporting production issues.

Required Skills

• 8+ years of professional experience or 6+ years with a Master's/Advanced Degree in a relevant analytical field.
• Proficiency in Python, Machine Learning, and Deep Learning principles.
• Hands-on experience with TensorFlow and PyTorch frameworks.
• Expertise in data manipulation using NumPy, Pandas, and SciKit-learn.
• Experience implementing MLOps practices.
• Familiarity with AWS SageMaker for model lifecycle management.
• Strong problem-solving skills to propose alternative solutions.

Preferred Skills

• Exposure to model serving engines like TensorFlow Serving or Triton Inference Server.
• Experience building Spark-based data and feature pipelines.

Key Skills

Education

Any Graduate