Job Description: ML OPS Engineer

You will be responsible for the deployment and maintenance of the group data science platform infrastructure, on which data science pipelines are deployed and scaled. To achieve this, you will collaborate with Data Scientists and Data Engineers from various business lines and the Global Technology Service infrastructure team (GTS).

Roles:

• Implement techniques and processes for supporting the development and scaling of data science pipelines.

• Industrialize inference, retraining, monitoring data science pipelines, ensuring their maintainability and compliance.

• Provide platform support to end-users.

• Be attentive to the needs and requirements expressed by the end-users.

• Anticipate needs and necessary developments for the platform.

• Work closely with Data Scientists, Data Engineers, and business stakeholders.

• Stay updated and demonstrate a keen interest in the ML OPS domain.

Environment:

• Cloud on-premise, AZure

• Python, Kubernetes

• Integrated vendor solutions: Dataiku, Snowflake

• DB: PostGreSQL

• Distributed computing: Spark

• Big Data: Hadoop, S3/Scality, MAPR

• Datascience: Scikit-learn, Transformers, ML Flow, Kedro

• DevOps, CI/CD: JFROG, Harbor, Github Actions, Jenkins

• Monitoring: Elastic Search/Kibana, Grafana, Zabbix

• Agile ceremonies: PI planning, Sprint, Sprint Review, Refinement, Retrospectives, …

• ITIL framework