The ML Engineer role encompasses designing building and maintaining machine learning systems developing training and deploying machine learning models and automating model deployment and management. As part of a larger team develops and integrates ML analytical components in business solutions and analytical models.
Skill requirements
Must Have:
- Relevant work experience in ML projects
- Relevant work experience in technologies and frameworks used in ML examples are Apache Airflow sklearn MLFlow TensorFlow
- Knowledge of MLOps architecture and practices
- Knowledge of data manipulation and transformation e.g. SQL
- Experience working in cloud environment data cloud platforms (e.g. GCP)
- Programming in Python
- Familiar with software engineering practices like versioning testing documentation code review
- Deployment and provisioning automation tools e.g. Docker Kubernetes Openshift CI/CD
Nice to Have:
- Experience with distributed systems and clusters for both batch as well as streaming data (S3/Spark/Kafka/Flink)
- Experience with monitoring and observability (ELK stack)
- Affinity with Advanced Analytics Data Science NLP
- Handson experience building complex data pipelines e.g. ETL
- System design and architecture
- Programming in a statically typed language e.g. Scala Java
- Good understanding of databases including RDBMS nonSQL and timeseries databases
- Experience with working in an agile/scrum way
- Being a committer to Open Source projects is a strong plus
Oferta de muncă a fost publicată la O lună în urmă
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