Description

Design and build engineering infrastructure to support enterprise machine learning systems at scale.

Responsibilities

• Design and create data pipelines and engineering infrastructure for large-scale ML systems.
• Convert data scientist models into production-ready machine learning systems.
• Develop and deploy scalable tools for ML training and inference.
• Apply software engineering rigor, including CI/CD and automation, to the ML lifecycle.
• Monitor model performance and troubleshoot errors, bugs, or system failures.

Required Skills

• 5+ years of experience with Python, Pandas, and Spark.
• Experience building large-scale data pipelines.
• Hands-on experience with Seldon and Triton.
• Experience implementing Jenkins CI/CD pipelines.
• Proficiency in SQL.
• Experience with machine learning versioning, model measurement, and auditability.
• Any Graduate degree.

Preferred Skills

• One year of experience with BigQuery, Hadoop, Hive, or Java.
• Experience with GCP, Azure, or REST APIs.

Key Skills

Education

Any Graduate