About the role
What you'll do:
Design, architect, implement and help operate the Machine Learning platform
by
o Develop and gain insight in the application architecture.
o Distill an abstract architecture into concrete design and influence the
implementation.
o Observing inefficiencies, both in cost and reliability, of existing
processes
o Researching alternative solutions using custom or existing open-source
technologies
o Designing replacement processes and components
o Implementing processes, extending and configuring open-source
components
Work with the ML DevOps and Support teams to operate the ML platform by
o Helping implement DevOps best practices of in-house and open-
source components
o Ensuring smooth operation via monitoring and alerting facilities
Work with the ML data scientists to
o Apply the appropriate software engineering patterns to build robust and
scalable systems for both model building and serving.
Technical Environment:
Software: Python 3.*
Application Architecture: Scalable, Resilient, Reactive, event-driven, secure multi-
tenant Microservices architecture
Cloud: Google Cloud
Frameworks/Others: Kubernetes, Kafka, Pub/Sub, Big Query, Apache Beam,
DataFlow, KubeFlow, Keras, TensorFlow, Flask.
What we are looking for:
Bachelor’s Degree in Computer Science is required, Masters is preferred
6+ years of software engineering experience building production software
Good working knowledge of Kubernetes and other virtualized execution
technologies
2+ years of experience working on at least one cloud environment, GCP
preferred
3+ years of Python programming experience with excellent understanding of
Object-Oriented Design & Patterns
2+ years of experience in building REST APIs
2+ Working Experience on Kafka and its integration with Cloud Services.