Data Engineer/MLOps 2

Key Responsibilities

• Assist in the design and development of MLOps pipelines for deployment and integration of ML models.
• Support data scientists and senior engineers in operationalizing machine learning models.
• Automate model training, testing, and deployment workflows using Python and shell scripting.
• Monitor models in production and flag performance issues or anomalies for resolution.
• Implement and maintain version control practices for ML models and data.
• Containerize ML services and applications using Docker.
• Set up and maintain Jenkins pipelines for continuous integration and delivery.
• Support Kubernetes-based deployment of ML workloads.
• Ensure adherence to security, data privacy, and governance standards.
• Document MLOps processes, workflows, and configurations for team knowledge sharing.
• Stay current with emerging MLOps tools, frameworks, and best practices.
• Participate in sprint planning, standups, and retrospectives in an Agile environment.
• Minimum experience required - 6 to 8 Years

Skills

• Hands-on experience or strong familiarity with MLFlow.
• Proficiency in Python; experience with automation and Shell/Bash scripting.
• Working knowledge of Docker for containerization.
• Experience with Jenkins or similar CI/CD tools.
• Basic understanding of Kubernetes for container orchestration.
• Understanding of machine learning concepts and the model development lifecycle.
• Familiarity with SDLC practices and Agile methodologies.
• Good analytical and problem-solving skills.
• Strong communication skills and ability to work collaboratively in a team.
• Bachelor's degree in Computer Science, Data Science, Information Technology, or a related field.
• Minimum experience required - 6 to 8 Years