The Lead Machine Learning Engineer should lead the development process to get the Machine Learning models into production, align business needs to engineering practices.
The lead ML Eng should guarantee the quality of the deliverable and decide about which architecture and technologies should the team use.
Lead the ML Engineering team.
Design and develop machine learning systems.
Develop Microservices to serve online and offline predictions.
Running machine learning tests and experiments.
Implement appropriate ML algorithms.
Monitoring Machine Learning Microservices.
Align architectures with business needs.
Integrate new microservices within the Rappi infrastructure and environment.
Automatic retrain Models.
Ensure that data science code is maintainable, scalable and debuggable.
Bring the best software development practices to the data science team and help them speed up their work.
Choose the best operational architecture together with the devops team.
Assert that all production tasks are working properly in terms of actual execution and scheduling.
Develop and implement new tools to reduce time to market of the data products.
Experience with microservices patterns.
Production troubleshooting (logs, monitoring, events).
Event driven development and domain driven development.
Working knowledge of containers (Docker).
Working knowledge of CI / CD practices (Jenkins, etc).
Working knowledge with Machine Learning or Data products.
Python (highly preferable)
Attention to details.
Great communication and reporting skills.
Critical thinking and problem solving.
Alta colaboración con diferentes áreas de la empresa.
Machine Learning serving tools : Kubeflow, MLFlow, Metaflow.
ETLs Tools : Airflow, Luigi.
Experience with latency monitoring : SignalFx, Locust y JMeter, NewRelic.
Cache : Reddis.
Experience with Kubernetes.
Experience with AWS.
Experience with Databases and data warehouses Snowflake, Postgres, Redshift, Kafka, Kinesis.
Experience with gunicorn, uwsgi.