Description

You will design, develop, and deploy production-grade machine learning systems as an individual contributor.

Responsibilities

• Develop and deploy machine learning systems into production environments.
• Build and manage Kubeflow pipelines independently while proposing technical standards.
• Implement feature engineering, feature stores, and audit capabilities.
• Apply standard software engineering methodologies including unit testing, test automation, and continuous integration.
• Conduct code reviews and produce technical design documentation.

Required Skills

• 7+ years of experience developing and deploying machine learning systems.
• Proficiency in the Python ecosystem, VS Code, and Jupyter Notebooks.
• Experience with Google Cloud Platform (GCP) and serverless code development.
• Hands-on experience with BigQuery or similar data warehouse technologies.
• Working knowledge of big data tools such as Hadoop, Spark, and Kafka.
• Experience with ML orchestration systems like Kubeflow, MLflow, Airflow, or TFX.
• Strong programming skills in Python, Java, C++, or Scala.
• Experience with AWS or GCP cloud environments.
• Degree in engineering, computer science, mathematics, or a related field.

Preferred Skills

• Deep expertise in object-oriented or object-functional scripting.

Key Skills

Education

Any Graduate