Machine Learning Ops Engineer

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.

Skills requirement

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