Lead Assistant manager

EXL Talent Acquisition Team

79d

About The Position

We are seeking an experienced MLOps Engineer to join our team. The ideal candidate will have a strong background in machine learning operations and will be responsible for deploying and managing machine learning models in a production environment. This role requires a deep understanding of MLOps practices and the ability to work collaboratively with data scientists and software engineers to ensure the successful deployment of ML solutions.

Requirements

• Bachelor's degree in Computer Science or a related field; advanced degree preferred.
• Minimum 5 years of experience working as an MLOps Engineer or similar role within a data-driven organization.
• Experience with Kubernetes and KubeFlow is mandatory.
• Strong understanding of machine learning concepts and algorithms.
• Proficiency in Python developing ML pipelines/scripts.
• Experience with popular MLOps toolkits such as Kubeflow Pipelines, TensorFlow Extended (TFX), MLflow, etc., is essential.
• Solid knowledge of containerization technologies like Docker and Kubernetes for deploying ML models at scale.
• Familiarity with cloud platforms like AWS/Azure/GCP for building scalable infrastructure solutions is highly desirable.
• Experience with version control systems like Git/GitHub for managing code repositories.
• Excellent problem-solving skills with the ability to analyze complex technical issues related to ML model deployments.

Responsibilities

• Design and implement MLOps pipelines for deploying machine learning models.
• Collaborate with data scientists to understand model requirements and deployment strategies.
• Manage and optimize the performance of ML models in production.
• Utilize containerization technologies for scalable model deployment.
• Monitor and troubleshoot ML model performance and reliability.

What This Job Offers

• Education Level: Bachelor's degree
• Number of Employees: 5,001-10,000 employees