Role:

As an MLOps Engineer, you will play a critical role in bridging the gap between data science and production, ensuring our machine learning models are deployed efficiently, reliably, and securely. You will work closely with data scientists, software engineers, and product managers to design, build, and maintain our MLOps infrastructure and pipelines. You will also be responsible for monitoring model performance, identifying and resolving issues, and continuously optimizing our ML workflows.

Responsibilities:

• Design, implement, and maintain MLOps pipelines for model training, deployment, and monitoring
• Experience with containerization technologies like Docker and container orchestration platforms like Kubernetes
• Experience with cloud platforms like AWS, Azure, or GCP
• Proficiency in CI/CD tools such as GitLab CI/CD, GitHub Actions, or CircleCI
• Solid programming skills in Python, Java, or Go and experience in scripting and automation**
• Experience with MLOps frameworks like Kubeflow, MLflow, Airflow, etc.
• Monitor model performance and identify potential issues
• Collaborate with data scientists and software engineers to improve the efficiency and reliability of our ML workflows
• Stay up-to-date on the latest MLOps trends and technologies

Desired Skills

• Bachelor's degree in Computer Science, Software Engineering, or related field
• 3+ years of experience as an MLOps Engineer or related role
• Strong understanding of machine learning concepts and algorithms
• Excellent communication and collaboration skills
• Ability to work independently and as part of a team
• Passion for building scalable and efficient solutions