ML Ops Engineer

As an ML Ops Engineer at unifyCX, you will play a crucial role in developing and managing the infrastructure required for ML workloads. Your responsibilities will include: - Model Deployment & Monitoring: Design, implement, and manage deployment pipelines for machine learning models. Monitor model performance to ensure stability and scalability of production systems. - Infrastructure Management: Develop and maintain infrastructure for ML workloads, utilizing cloud services, containerization, and orchestration tools. - Automation: Automate ML lifecycle processes including data ingestion, model training, testing, and deployment. - Collaboration: Work closely with data scientists and engineers to understand model requirements and ensure seamless integration into production systems. - Version Control: Implement and manage version control systems for ML models, datasets, and code. - Performance Tuning: Optimize ML models and systems for performance, cost, and efficiency. Troubleshoot and resolve issues in production environments. - Security & Compliance: Ensure ML systems and processes comply with security policies and regulatory requirements. - Documentation & Reporting: Document processes, best practices, and workflows. Provide regular reports on system performance, issues, and improvements. Qualifications required for this role include: - Bachelor's degree in Computer Science, Data Engineering, or a related field. - 3+ years of experience in ML Ops, DevOps, or Data Engineering. - Strong proficiency in Python and Linux-based systems. - Hands-on experience with ML frameworks like TensorFlow, PyTorch, and Scikit-learn. - Proficiency in containerization (Docker) and orchestration (Kubernetes). - Experience with cloud platforms such as AWS Sagemaker, GCP Vertex AI, or Azure ML. - Familiarity with CI/CD pipelines and infrastructure-as-code tools like Terraform and CloudFormation. Preferred skillsets for this role may include experience with feature stores, knowledge of data pipelines and orchestration tools, background in MLOps frameworks, experience in model monitoring and drift detection, and a strong understanding of software engineering best practices and agile workflows.,