What you’ll Do:
- Building, maintaining, and improving infrastructure related to deployment and productionisation of machine learning (ML) models built for both internal and external use
- Developing standards and protocols for deployment, QA, QC, of ML models
- Consulting and advising data scientists on code, structural, or architectural improvements to ML models
- Building and maintaining monitoring, anomaly detection, incident management infrastructure and protocols including but not limited to data quality, various types of model drift, A/B testing of model updates, and production incidents.
- Troubleshoot and implement long-term solutions to problems related to deployed ML/optimization models.
What you’ll Need:
- At least 2 years of experience in systems design and architecture, software engineering, database administration, or machine learning development flow.
- A hacker’s mindset – the ability to build simple but clever and elegant solutions to new problems within significant resource, operational and time constraints through deep understanding of the business, creative problem solving, and a wide range of expertise in data, analytics, automation, programming, and prototyping.
- A good understanding of systems architecture, and the ability to implement this understanding in maintenance and deployment of ML models scalably.
- Systematic approach to problem solving especially when it comes to optimizing existing projects or implementing new ones such that it can be easily built upon in the future.
- Ability to read, understand, and implement algorithms, approaches, and methods from published research
- Familiar with Python/Go in production environments.
- Familiarity with Apache Spark is a plus.