We are looking for an experienced engineer focusing on observability and ALM or data sciences to promote machine learning engineering.
The person is passionate about building high-quality data products and processes, as well as supporting production real-time performance observability.
As a Machine Learning Engineer, you will be handling Data Science projects from the preparation stage until production and beyond. Youll be coordinating with stakeholders and play a major role in driving the business by promoting the models performance and lifecycle processes, which are the core of our product.
Responsibilities:
Data wrangling – supporting and building data requirements for data science research as well as model training, validation and testing.
Delivering end-to-end ML products – model performance development, training, validation, and testing, as well as version control.
Promote engineering best practices – code and model versioning, CI/CD processes, rollout and DRP procedures.
Create monitors, alerts, and dashboards – managing model performance in production.
Collaborate with our product, data science, and engineering teams to solve problems and identify trends and opportunities.
Implementation and POCs of open source tools and frameworks for MLE/MLOps and parallel processing.
The person is passionate about building high-quality data products and processes, as well as supporting production real-time performance observability.
As a Machine Learning Engineer, you will be handling Data Science projects from the preparation stage until production and beyond. Youll be coordinating with stakeholders and play a major role in driving the business by promoting the models performance and lifecycle processes, which are the core of our product.
Responsibilities:
Data wrangling – supporting and building data requirements for data science research as well as model training, validation and testing.
Delivering end-to-end ML products – model performance development, training, validation, and testing, as well as version control.
Promote engineering best practices – code and model versioning, CI/CD processes, rollout and DRP procedures.
Create monitors, alerts, and dashboards – managing model performance in production.
Collaborate with our product, data science, and engineering teams to solve problems and identify trends and opportunities.
Implementation and POCs of open source tools and frameworks for MLE/MLOps and parallel processing.
Requirements:
3+ years experience as a software engineer
High level programming skills in Python and SQL
Experience working with Spark & Airflow frameworks on large datasets
Experience with machine learning pipelines – an advantage
Experience as a backend engineer or devops engineer – an advantage
3+ years experience as a software engineer
High level programming skills in Python and SQL
Experience working with Spark & Airflow frameworks on large datasets
Experience with machine learning pipelines – an advantage
Experience as a backend engineer or devops engineer – an advantage
This position is open to all candidates.
