Job Title: ML Ops Engineer
Responsibilities:
Model Deployment & Integration:
Design, develop, and manage automated pipelines for deploying machine learning models into production.
Ensure smooth integration between model development, data, and application teams.
Implement model versioning and rollback strategies to facilitate easy model updates and troubleshooting.
Infrastructure Automation:
Build and maintain scalable infrastructure using tools like Kubernetes, Docker, and cloud platforms (AWS, Azure, GCP).
Automate the deployment process and manage model serving environments.
Design and optimize cloud-native solutions to ensure scalability and performance under heavy workloads.
Monitoring and Maintenance:
Continuously monitor the performance and health of deployed models in production settings.
Implement real-time logging, alerting, and monitoring systems to ensure models effectiveness over time.
Detect, troubleshoot, and resolve issues such as model drift, degradation, and inefficiencies.
Collaboration with Data Scientists & DevOps:
Work closely with data scientists to ensure that models are production-ready and meet system requirements.
Collaborate with DevOps teams to integrate MLOps tools and practices into the CI/CD pipeline.
Optimize model performance by coordinating with various teams to manage the lifecycle of machine learning models.
Model Retraining & Continuous Improvement:
Automate and manage model retraining processes based on incoming current data or changing business needs.
Create frameworks for evaluating and improving model accuracy, efficiency, and robustness.
Security & Compliance:
Ensure the security of machine learning systems, including data protection, model access control, and sensitive data handling.
Ensure compliance with relevant regulatory requirements related to data privacy and security.
Performance Optimization:
Work on optimizing models and system performance for faster inference