MLOps Engineer

MLOps, MLFlow, Python, Docker, Jenkins, Kubernetes, Automation, CI/CD, SDLC, Deployment, Machine Learning

• Design and develop MLOps pipelines for deployment and integration of ML models.
• Collaborate with data scientists and engineering teams to operationalize machine learning models.
• Automate model training, testing, and deployment workflows using Python and Shell scripting.
• Monitor models in production and identify performance issues or anomalies.
• Implement and maintain version control practices for ML models and datasets.
• Containerize ML services and applications using Docker.
• Build and maintain CI/CD pipelines using Jenkins or similar tools.
• Support Kubernetes-based deployment and orchestration of ML workloads.
• Ensure adherence to security, data privacy, and governance standards.
• Document MLOps workflows, configurations, and best practices.
• Stay updated with emerging MLOps tools, technologies, and industry trends.
• Participate in Agile ceremonies including sprint planning, standups, and retrospectives.

• 24 years of experience in MLOps, DevOps, or software engineering with exposure to Machine Learning.
• Hands-on experience or strong familiarity with MLFlow.
• Strong proficiency in Python and Shell/Bash scripting.
• Experience with Docker for containerization.
• Familiarity with Jenkins or other CI/CD tools.
• Basic understanding of Kubernetes and container orchestration.
• Understanding of machine learning concepts and the ML lifecycle.
• Familiarity with SDLC practices and Agile methodologies.
• Strong analytical and problem-solving abilities.
• Good communication and collaboration skills.
• Bachelors degree in Computer Science, Information Technology, Data Science, or a related field.