Ml Ops Engineer

Key Responsibilities

• Model Deployment: Design, build, and maintain scalable and reliable deployment pipelines for machine learning models.
• Pipeline Development: Design, implement, and maintain end-to-end automated ML pipelines, covering data preprocessing, training, validation, and deployment stages.
• Infrastructure Management: Manage cloud and on-premise infrastructure to ensure optimal performance of ML applications.
• Monitoring and Maintenance: Implement monitoring solutions to track model performance and data drift, and establish procedures for model retraining and updating.
• Collaboration: Work closely with data scientists, software engineers, and product teams to understand requirements and ensure smooth integration of ML models into applications.
• Automation: Automate ML lifecycle processes, including data preprocessing, model training, validation, and deployment.
• Documentation: Maintain clear documentation of ML workflows, tools, and best practices to facilitate knowledge sharing and onboarding.
• Security and Compliance: Ensure that all ML systems meet security and compliance standards.

Qualifications

• Experience: Minimum of 5 years of experience in MLOps, data engineering, or a related field.
• Technical Skills:
• Proficiency in Python.
• Experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with containerization (Docker, Kubernetes) and orchestration tools.
• Solid understanding of CI/CD practices and tools (e.g., Jenkins, GitLab CI).
• Knowledge of cloud platforms (AWS, Azure, GCP) and their ML services.
• Soft Skills:
• Excellent problem-solving skills and a proactive mindset.
• Strong communication skills to collaborate effectively with cross-functional teams.
• Ability to mentor junior team members and share knowledge.