Description

Design, build, and deploy machine learning models and supporting infrastructure. Own the end-to-end ML lifecycle from data ingestion to production hosting.

Responsibilities

• Curate and maintain data repositories from public and internal sources for model development and evaluation.
• Develop data ingestion and transformation scripts to support model requirements.
• Build end-to-end ML pipelines, including training, deployment, and hosting in production environments.
• Implement engineering best practices such as unit testing, monitoring, and logging.
• Manage ETL processes and data stores to ensure reliable data flow.

Required Skills

• 5+ years of professional experience in software or ML engineering.
• Fluency in the Python ML/data science stack, specifically PyTorch, Scikit-learn, and Pandas.
• Proficiency in cloud engineering across AWS, Azure, or GCP.
• Experience with Infrastructure-as-Code using Terraform or equivalent tools.
• Hands-on experience with ETL/data pipelines and data stores.
• Facility with web application technologies like FastAPI.
• Experience with UI development.
• Bachelor's degree or equivalent graduate qualification.

Preferred Skills

• Experience deploying models to production environments at scale.

Key Skills

Education

Any Graduate