Responsibilities
- Design and develop the engineering infrastructure to support all ML-related activities.
- Work closely with Data Scientists and other stakeholders to define the ML system requirements for experimentation and production.
- Define internal quality processes in the team, including code reviews, testing methodologies, and deployment processes.
- Work on various tasks : architecture and design, coding, monitoring, working with cloud services, and working closely with DevOps.
- Continuously explore new tools and practices in the MLOps and cloud processing world that can be harnessed for our mission to build a smooth ML development and serving pipeline.
- Identify improvement opportunities, such as automating manual processes and cost-reduction opportunities.
Minimum Qualifications
- B.Sc. in Computer Science or equivalent
- 5+ years of experience in designing, developing, and deploying data pipelines and machine learning models in production at scale
- Experience with cloud technologies (GCP / AWS / Azure)
- Production-grade Python coding experience and familiar with ML pipelines tools such as VertexAI or Sagemker
- Well-acquainted with CI / CD pipelines
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