Lead Software Engineer - ML OPS Engineer

Responsibilities

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

Profile required

Technical Skills:

• Python
• FastApi,
• SqlAlchemy
• Numpy, Pandas, Scikit-learn, Transformers
• Kubernetes, Docker
• Pytest
• CI/CD: Jenkins, Ansible, GitHub Action, Harbor, Docker

 Soft Skills:

·         Client Focus:

Demonstrate strong listening skills, understanding, and anticipation of user needs.

·         Team Spirit:

Organize collaboration, workshops to find the best solutions.

Share expertise with colleagues to find the most suitable solutions.

·         Innovation:

Propose innovative ideas, solutions, or strategies, and think out the box.

Prefer simplicity over complexity.

·         Responsibility:

Take ownership, keep commitments and respect deadlines.