Advanced AI solutions are constantly evolving, and we need talented professionals to contribute to their operational success.
Key Responsibilities:
* Design and Maintain Infrastructure: Set up robust CI/CD pipelines and cloud infrastructure using Terraform with a strong focus on reliability, security, and scalability.
* Containerized ML Applications: Manage containerized ML applications with Docker; familiarity with Kubernetes is a plus.
* ML Lifecycle Support: Monitor model performance, optimize infrastructure, and share best practices across the team to foster continuous improvement.
The ideal candidate has 3-5 years of experience in data engineering, DevOps engineering or similar roles. They possess programming expertise in Python in a machine learning context, strong knowledge of Docker and cloud infrastructure, and experience with Azure is a plus. Experience in data engineering contexts is beneficial though heavy ETL is not the primary focus.
Bonus points are given for experience with MLOps tools, knowledge of security best practices in cloud-based ML environments, and interest in GenAI, LLMs, or prompt engineering.
As a member of our GenAI team, you will work closely with data scientists and ML engineers to bring models into production. Your work will directly contribute to the operational success of advanced AI solutions.