Description:

You will be responsible for designing, building, and maintaining scalable machine learning pipelines, deploying models to production environments, and ensuring the reliability and scalability of ML operations.
The role involves managing infrastructure, implementing CI/CD pipelines, containerization, API management, monitoring, security, collaboration with data scientists, and performance optimization.
This job reports to the Manager – AI.
You will design, build, and maintain scalable ML pipelines and deploy models to test and production environments.
You will set up and manage cloud and on-premises infrastructure to support ML operations.
You will develop and maintain CI/CD pipelines for ML models and automate build, test, and deployment processes.
You will utilize Docker and Kubernetes for deploying ML models and manage containers for smooth operation and scalability.
You will develop and manage APIs to support ML models, monitor and secure API calls, and ensure seamless integration with external applications.

Requirements:

A Bachelor’s or master’s degree in computer science, Engineering, Data Science, or a related field is required.
You must have 4 years of proven experience as an ML Ops Engineer or in a similar role in a production environment.
Experience with the Azure cloud platform is required, and AWS experience is a plus.
You must have experience with containerization technologies such as Docker and Kubernetes.
Experience with API management tools, specifically Kong, is required.
Strong programming skills in Python are necessary.
Proficiency in CI/CD tools is required.
Familiarity with machine learning frameworks such as TensorFlow and PyTorch is necessary.
A strong understanding of DevOps practices and principles is required.
Excellent problem-solving skills and attention to detail are essential.
Strong communication and collaboration skills are necessary.

This position offers the opportunity to work remotely, providing flexibility in your work environment.
You will be part of a dynamic team that collaborates closely with data scientists and engineers throughout the ML lifecycle.
The role provides the chance to work with cutting-edge technologies in machine learning and cloud infrastructure.
You will have the opportunity to implement best practices for ML operations, enhancing your professional development.
The position includes the potential for career growth within the organization as you gain experience and expertise in ML operations.