Mlops Developer

• Lead the transition of models from Jupyter notebooks into production-ready microservices
• Build and manage the infrastructure required to support LLMs and RAG pipelines at scale
• Implement logging and alerting systems to detect 'model drift' or accuracy drops caused by changes in factory sensor data
• Ensure all AI tools and internal chatbots meet enterprise security standards and are integrated with company-wide authentication
• Optimize AI services to handle high-frequency data from multiple production lines simultaneously

• Proficiency with ML lifecycle tools like MLflow, Kubeflow, or DVC for model versioning and tracking
• Strong Python skills, particularly for creating robust API endpoints and managing microservices
• Experience implementing SSO/MFA for AI services and setting up real-time monitoring for model performance/drift
• 2-5+ years of professional experience in DevOps or MLOps, with a specific focus on deploying and maintaining machine learning models in production
• Deep expertise in building automated CI/CD pipelines specifically for ML (using Git, Jenkins, or GitHub Actions) and containerization with Docker
• A Bachelor’s Degree in Computer Science, Software Engineering, or a related field

• Experience with Kubernetes for managing large-scale GPU workloads
• Experience managing hybrid environments (On-premise servers vs. Azure/AWS)
• Knowledge of automated testing for AI/ML (Unit tests for model logic)

Insurance