Key Responsibilities:

Collaborate with data scientists and engineers to understand the machine learning model requirements and develop production-ready solutions.
Design, implement and maintain the infrastructure for machine learning models in a distributed environment.
Develop and implement monitoring and logging systems to track model performance and identify issues.
Build and maintain data pipelines to support the training and deployment of machine learning models.
Identify and resolve infrastructure issues to ensure smooth and efficient operation of machine learning models.
Work closely with DevOps and IT teams to ensure the scalability, reliability, and security of machine learning systems.
Automate deployment and testing processes to streamline the deployment of new models and updates.
Stay updated with the latest advancements in machine learning and implement best practices in infrastructure and operations.

Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
Minimum of 3 years of experience in a similar role, with a strong focus on machine learning infrastructure and operations.
Solid understanding of machine learning concepts and techniques, with hands-on experience in deploying and managing machine learning models in a production environment.
Proficiency in programming languages such as Python, Java, or Scala, and experience with data processing frameworks like Spark or Hadoop.
Experience with cloud computing platforms such as AWS, Azure, or Google Cloud Platform.
Strong knowledge of Linux operating systems and shell scripting.
Familiarity with DevOps tools such as Docker, Kubernetes, or Ansible.
Excellent problem-solving skills and ability to troubleshoot complex issues in a timely manner.
Strong communication and collaboration skills to work effectively with cross-functional teams.

Join our team and be a part of our exciting journey towards building intelligent and data-driven solutions.

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