MLOps Engineer

Key Skills MLOps, MLFlow, Python, Docker, Jenkins, Kubernetes, Automation, CI/CD, SDLC, Deployment, Machine Learning Key Responsibilities 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.

Required Skills & Experience 2–4 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. Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field.