Value Quantification : Pre-Model Development, Model Provisioning: Kubernetes, Kibana, Model Monitoring, Cloud Computing, Python/PySpark, SAS/SPSS, Great Expectation, Evidently AI, Deployment Strategies (A/B, Blue green, Canary), Model testing, Tools(KubeFlow, BentoML), Integration testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Value Quantification: Post-Model Deployment, Model Experimentation, R/ R Studio
Job requirements
Drift Frame Work : Framework for detecting drift Automatically monitor track accuracy and trigger model retraining and notifications to restore previous accuracy levels ML Generalist: Data Scientist with MLOPS Development and maintenance of ML pipeline ML Engineer focusing on experimentation and tracking Responsibilities: Model Development: Develop machine learning models and algorithms to solve business problems, leveraging techniques such as supervised learning, unsupervised learning, and deep learning. Deployment and Integration: Deploy machine learning models into production environments and integrate them with existing systems and workflows. Performance Optimization: Optimize machine learning models for scalability, efficiency, and performance, considering factors such as latency, throughput, and resource utilization. Monitoring and Maintenance: Monitor model performance in production, identify and diagnose issues, and implement solutions to ensure continued reliability and effectiveness. Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to understand business requirements and deliver solutions that meet stakeholders' needs. Research and Innovation: Stay up-to-date with the latest advancements in artificial intelligence and machine learning research, and explore new techniques and methodologies to improve model performance and capabilities.
