Machine Learning Ops Engineer
October 17
Airflow
Apache
Bash
Cloud
Distributed Systems
Docker
ETL
Google Cloud Platform
Java
Kafka
Kubernetes
OpenShift
Python
Scala
Scikit-Learn
Spark
SQL
Tensorflow
Description
• An MLOps Engineer is responsible for managing the lifecycle of machine learning models, ensuring they are deployed, monitored, and maintained effectively. • The MLOps Engineer supports the development, training, and deployment of machine learning models, and key skills involve CI/CD Pipelines, Model Deployment, Monitoring and Maintenance, Automation and Performance Optimization.
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 (e.g. GCP) • Programming in Python • Experience with monitoring and observability (ELK stack) • 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) • Affinity with Advanced Analytics, Data Science, NLP • Hands-on experience building complex data pipelines e.g. ETL • System design and architecture • Bash scripting and Linux systems administration • Programming in a statically typed language, e.g. Scala, Java • Experience with building distributed, large scale and secure applications • Experience with working in an agile/scrum way • Being a committer to Open-Source projects is a strong plus
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