Job Description – Junior ML Ops engineer
Experience : 1-3 Years
Primary Skills: Programming: Python / R / Jupyter Notebooks / Airflow
DB: SQL/NoSQL
Secondary Skills: Kubernetes, VertexAI, AutoML, Python
Key responsibilities:
Design and implement cloud solutions, build MLOps on cloud (GCP)
Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools;
Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
Data science models testing, validation and tests automation.
Communicate with a team of data scientists, data engineers and architect, document the processes.
Required Qualifications:
Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (GCP)
Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes, OpenShift.
Programming languages like Python, Go, Ruby or Bash, good understanding of Linux, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc.
Ability to understand tools used by data scientist and experience with software development and test automation.
Fluent in English, good communication skills and ability to work in a team.
Desired Qualifications:
Bachelor’s degree in Computer Science or Software Engineering
Experience in using GCP services.
Good to have Google Cloud Certification
