Description

You will bridge the gap between data science and production by building and maintaining scalable machine learning infrastructure.

Responsibilities

• Design and create data pipelines and engineering infrastructure to support enterprise machine learning systems at scale.
• Convert models built by data scientists into production-ready machine learning systems.
• Develop and deploy scalable tools and services for machine learning training and inference.
• Apply software engineering rigor through CI/CD, automation, and best practices to machine learning workflows.
• Monitor model performance and troubleshoot errors, bugs, or system failures.

Required Skills

• 5+ years of experience with Python, Pandas, and Spark.
• Proven experience building large-scale data pipelines.
• Experience with Seldon, Triton, and Jenkins CI/CD.
• 1 year of experience with BigQuery, Hadoop, SQL, Hive, GCP/Azure, Java, and REST.
• Ability to interpret model results and understand data relationships relative to problem solving.
• Knowledge of model versioning, auditability, measurement, and data security.
• Any graduate degree.

Key Skills

Education

Any graduate