Develop end-to-end (Data/Client) Ops pipelines based on an in-depth understanding of AWS cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably.
Develop systems, tools, & processes to monitor client models in production, monitoring drift and performance, and initiating retraining and validation as necessary.
Develop and maintain Model Registry, Feature Store, AWS Data Catalog, and documentation for internal business customers.
Qualifications
Bachelor's degree in engineering, computer science, mathematics, or related field.
At least 5 years of production-grade Python framework development experience.
Prior experience in delivering data-science projects including operationalizing in cloud platforms using AWS SageMaker and putting together an MLOps framework for the client.
Experience in using core analytics methods, Predictive Modeling, Machine Learning, Simulation, and Optimization.
Expertise in a variety of machine learning and statistical techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Excellent communication and client-facing skills, with good business acumen and hands-on coding experience.
Experience in working with clients in the Life sciences, healthcare, especially pharma industry experience.