Principal MLOps Engineer leading design and optimization of machine learning infrastructure at Wood Mackenzie. Collaborating with data science and engineering teams to ensure robust automated ML lifecycles.
About the role
- Backend Engineer optimizing ML infrastructure to enhance performance at an energy forecasting startup. Collaborating with ML engineers to improve tooling and infrastructure for large-scale data processing.
Responsibilities
- Work closely with ML engineers to help speed up their work through better infrastructure and tooling.
Requirements
- Strong backend software engineer who has worked with ML engineers and has helped solve their problems
- Strong distributed systems and infrastructure skills. Is comfortable standing up services in AWS/GCP, scaling and debugging Kubernetes services, writing Terraform, and working with orchestration tools like Flyte, Airflow, or Temporal.
- Strong software engineering skills. Being able to write easy-to-extend and well-tested code.
- Has worked with large-scale data, and makes good choices on data storage and schema design (relational databases, data warehouses, object storage, timeseries data)
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