Senior AI/ML Engineer

Giggso, founded in 2017 in Michigan, focuses on helping enterprises successfully navigate digital transformation. As a certified Minority Business Enterprise (MBE), Giggso brings diverse perspectives to solving complex AI and engineering challenges for Fortune 500 clients worldwide. The company specializes in moving organizations from experimental AI pilots to sustainable AI profit centers. Giggso’s core expertise lies at the intersection of Governance, Risk, Compliance (GRC) and Engineering, turning AI governance from a bottleneck into a competitive advantage. Learn more at www.giggso.com. To join our team, you should possess the below skills: Experience in productizing and deployment of Machine Learning solutions. Thorough understanding of the full ML pipeline, starting from data collection to model building to inference. Thourough understanding of model monitoring (detecting model variations, etc.) and also the retraining/redeployment process Experience with Python and at least a few frameworks/libraries (TensorFlow/ Keras / PyTorch / spaCy / fastText / Scikit-learn etc.) Experience or Knowledge in MLOPS and Model OPS. NLP (Natural Language Processing) experience, including text classification, entity extraction, and content summarization. Strong verbal and written communication skills to express data science concepts and solutions to product managers, data scientists, business partners of varying technical levels, and influence and negotiate to reach alignment on executing strategies and improving models. Your responsibilities: Develop, train, deploy and monitor Machine Learning models. Utilize published/existing models to meet business requirements, making necessary tweaks for improving efficiencies and adapting to use-case variations. Optimize machine learning model training and inference time. Perform Machine Learning model training, deployment, monitoring, and retraining. Independently handle end-to-end tasks, including data collection, preparation/annotation, and validation of outcomes. Define and develop ML infrastructure to enhance the efficiency of ML development workflows. Collaborate with other Engineers, data scientists, product managers, and engineers to formulate innovative ideas, and test / implement them through advanced data science techniques.