Machine Learning Engineer
October 17
Airflow
Apache
Cloud
Distributed Systems
Docker
ETL
Google Cloud Platform
Java
Kafka
Kubernetes
Open Source
OpenShift
Python
RDBMS
Scala
Scikit-Learn
Spark
SQL
Tensorflow
Go
Description
• Machine Learning Engineer role involves designing, building, and maintaining machine learning systems • Developing, training, and deploying machine learning models • Automating model deployment and management • Integrating ML analytical components in business solutions and analytical models
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 • Hands-on 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, non-SQL and time-series databases • Experience with working in an agile/scrum way • Being a committer to Open Source projects is a strong plus
Apply Now