Lead Software Engineer - ML OPS Engineer

• 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