Implements machine learning (ML) models for production. Ensures the readiness of machine learning models for deployment in production. Automates machine learning workflows. Creates infrastructure and frameworks to monitor the performance of machine learning models in deployment. Evaluates potential data quality, security, and/or privacy issues and their impacts on modeling. Provides troubleshooting and debugging support. Addresses issues in machine learning infrastructure and workflows. Collaborates with stakeholders to integrate machine learning models into new or extant systems. Develops, maintains, and refines tools, platforms, and services for internal use. Develops efficient, bug-free code from scratch. Maintains familiarity with current developments in the machine learning field and integrates knowledge into model development.
Key
Responsibilities
- Machine
- Learning and Data Modeling – Model Productionization:
- Utilizes
- machine learning (ML) and software development knowledge to implement ML models
- for production.
- Engages
- in transforming machine learning prototypes into production-ready models.
- Collaborates
- with multiple stakeholders, such as Development Leads, Product Management,
- Operations, and Release Management, to make, adopt, and communicate technical
- decisions, and shape the development and delivery of software.
- Model
- Development and Deployment – Model Deployment:
- Ensures
- ML model readiness for deployment by scaling models, cleaning model code, and
- ensuring production quality standards are met.
Show the full description
- Automates
- machine learning workflows, from data extraction, transformation, and loading
- (ETL) to model deployment and monitoring, to establish the continuous
- integration and continuous delivery of machine learning solutions.
- Model
- Development and Deployment – Model Performance:
- Creates
- infrastructure and frameworks to monitor the performance and alignment with
- design criteria of trained models and/or systems.
- Proactively
- monitors the performance of deployed models and troubleshoots independently or
- in collaboration with Data Science.
