Senior MLOps Engineer
Job description
Senior MLOps Engineer :
Role:
Cloud Solutions Design and Implementation:
Develop and implement robust cloud solutions for ML applications, including Gen AI, utilizing AWS, Azure, or GCP.
Build MLOps pipelines on both cloud and on-premise solutions.
CI/CD Pipeline Orchestration:
Design and manage CI/CD pipelines using GitLab CI, GitHub Actions, Circle CI, Airflow, or similar tools.
Data Pipeline Implementation:
Construct efficient data pipelines for both streaming and batch data.
Establish and maintain data lakes and feature stores.
Data Science Model Lifecycle Management:
Review data science models and execute code refactoring, optimization, containerization, deployment, versioning, and continuous monitoring.
Conduct testing, validation, and automation of data science models.
Collaboration and Documentation:
Work closely with a multidisciplinary team of data scientists, data engineers, and architects.
Document and communicate processes effectively.
Background:
Bachelors degree + 5 years or Master’s degree + 3 years of relevant experience or PhD degree in computer science, electrical engineering, or related field.
#EXP[7-15]
Practical experience with setting up and managing ML training and data pipelines in production.
Solid knowledge in at least a few of the following tools: Docker, Kubernetes, Kubeflow, Airflow, Mesos, SLURM, MLFlow, Metaflow.
Experience with data versioning tools like DVC.
Familiarity with Infrastructure as Code (IaC) to define and deploy infrastructure.Strong software development skills in Python.
Preferred Qualifications:
Practical experience with Linux, version control systems (Git), build systems (Make, CMake, Autotools), and code review tools (Gerrit, Gitlab, Bitbucket).
Knowledge in C/C++, JavaScript.
Job Features
| Job Category | MLOps Engineer |