Job Description :
Hello All, Greetings!! We have an immediate opening for Machine Learning / Sr. Machine Learning Engineers at San Antonio, TX. Please go through the requirement and reply with your updated profile, contact details, and your availability if you would be interested in it. Title: Machine Learning / Sr. Machine Learning Engineers Location: Initially Remote /San Antonio, TX Duration: Long term No. of openings: 4 Description: The primary responsibility of this role is to create design and implement processes to streamline AI/ML operations right from Data supply strategy to Data discovery to Model training and development to deployment of AI services. ML Ops Engineer will help implementation partner in building services and workflows to aid in the quick and widespread deployment of AI Services on an enterprise scale. The ML Ops Engineer will work with data scientists, platform/data engineers, and domain experts to design and implement automated pipelines CI/CD, Continuous training, Model continuous delivery prediction service and monitoring. Following are the additional responsibilities as given below: Responsibilities Hardening and operationalizing ML models and the data pipeline by training Models on large data sets Build and support reliable deployment capabilities that increase deployment frequency ML Ops practice to ensure compliance and review acceptance for all stages of the process Develop Framework that support frequent Model Retraining and deployment Auditable process that can help ease exception wavers for operationalizing models Building a deployment framework, ensuring compliance and review acceptance for all stages of the process. Auditable and Sustainable ML Ops practice for repeatable deployments after re training Models time and again, accelerate ML Model Governance Work with data scientists, DevOps, data engineers/SMEs from domain to understand how data availability and quality affects model performance Design and implement end-to-end machine learning pipelines accounting for the variability in data sources and collection policies, data analysis and feature extraction methodologies, modeling frameworks for cloud and edge, and serving infrastructure Using Databricks MLFlow, DevOps and Azure ML build end-end automated Model deployment and management pipeline. Design and implement steps for continuous training with new data sets, continuous performance monitoring and alerting on degrading performance. Design target state model serving layer - API's / prediction service and end user consumption. Use configuration and API management to abstract and automate most manual tasks both pre-modeling and post-deployment Hands on experience & expertise to define the ML Ops strategy as well as helping team implement the same Hands on experience with Azure ML, Databricks MLFlow and Azure Devops AL/ML technologist with in designing & building AIML Strategy & solutions , Model development & execution lifecycle, ML Ops Thanks &