Overview
An applied analytics team is taking several machine learning models from experimentation into production and needs an engineer who can bridge modelling and software delivery. The focus is on reliable pipelines, model deployment and measurable performance rather than research work alone.
Responsibilities
- Move selected machine learning models from experimentation into production.
- Build reliable data and deployment pipelines around trained models.
- Implement monitoring for model behaviour and service performance.
- Work with analysts and software engineers to improve production quality and measurable model outcomes.
Requirements
- 4+ years of experience developing machine learning solutions.
- Strong Python skills with practical software engineering habits.
- Experience deploying models through cloud or container based environments.
- Good understanding of model evaluation, monitoring and data preparation.
- Familiarity with MLflow, Kubeflow or comparable tooling.
- Experience taking prototypes into maintained production services.