Description

You will bridge the gap between machine learning development and production deployment.

Responsibilities

• Productionize machine learning models using established MLOps principles and deployment strategies.
• Design and implement statistical modeling and machine learning algorithms.
• Manage end-to-end ML lifecycles using tools like MLflow, Kubeflow, or TensorFlow Serving.
• Build and maintain ETL processes and data warehousing solutions using SQL.
• Deploy and manage machine learning models across major cloud providers including AWS, Azure, or Google Cloud.

Required Skills

• 6-9 years of experience in data science with a focus on MLOps.
• Proficiency in Python for data analysis and machine learning.
• Deep understanding of machine learning algorithms and model evaluation techniques.
• Hands-on experience with MLOps platforms such as MLflow, Kubeflow, or TensorFlow Serving.
• Experience with cloud platforms (AWS, Azure, or Google Cloud).
• Solid understanding of data engineering, ETL processes, and SQL.
• Proficiency with Git for version control.
• Bachelor's or master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.

Preferred Skills

• Experience with big data tools like Hadoop, Spark, or Kafka.
• Familiarity with Docker, Kubernetes, Jenkins, Terraform, or CI/CD pipelines.
• Ability to translate business requirements into technical model designs.

Key Skills

Education

Bachelor's degree in Computer Science