Lead Software Engineer - ML OPS Engineer

Job description

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