Description

You will engineer and scale machine learning and data platforms for enterprise analytics.

This role is hybrid.

Responsibilities

• Design, develop, and deploy scalable machine learning models using frameworks like PyTorch.
• Re-engineer and optimize legacy models into efficient, production-grade implementations.
• Design and optimize distributed data pipelines using Spark-based platforms.
• Build and refactor ETL/ELT workflows for performance and scalability.
• Participate in migrating on-prem analytics platforms to cloud ecosystems.

Required Skills

• 5+ years of professional experience in software engineering or data engineering.
• Strong programming skills in Python.
• Hands-on experience with PyTorch or similar deep learning frameworks.
• Expertise in Spark-based data processing, preferably Databricks.
• Strong SQL proficiency.
• Experience with cloud data warehouses such as Snowflake.
• Proven ability to build and optimize ETL/ELT pipelines.
• Familiarity with distributed computing and performance tuning.
• Experience working in cloud environments (AWS, Azure, or GCP).

Preferred Skills

• Experience modernizing legacy codebases (e.g., C++, R).
• Knowledge of model risk management or validation frameworks.

Key Skills

Education

Any Graduate