Responsibilities

Work with data scientists to refine the ML model and scale it up
Create and maintain ML model training/prediction pipelines in production
Create re-usable tools and frameworks for ML model deployment and monitoring
Mentor junior colleagues, conduct internal workshops and external meetups, participate in external conferences and give talks.

Requirements

Solid understanding of machine learning models and related mathematics
Solid understanding of engineering processes and principles.
Strong understanding of both object-oriented and functional programming concepts and languages.
Experience building production data pipelines for model training/prediction
Experience working with large data sets, coming from varied sources
Experience working with open-source ML libraries such as Tensorflow, PyTorch and XGBoost
Experience working with cloud-based ML model deployment and automation tools (such as Airflow, Docker)
Experience working with engineering tools for infrastructure and deployment (such as Docker, Kubernetes)
Familiarity with data engineering technologies (Kafka/Flink/Spark etc)
Typical background: Bachelors or Masters in computer science with 4+ years of experience working as a Software Engineer or Machine Learning Engineer in a product company
Familiarity with infrastructure automation tools like Terraform
In-depth understanding of data engineering technologies (Kafka/Flink/Spark etc)