Erias Ventures was founded to serve its customers with an entrepreneurial mindset. We value creative problem-solving , open communication , and empowering our employees to make decisions and put forth new ideas. Our staff includes technical experts working across multiple disciplines, bringing diverse perspectives to every project. We are seeking engineers who wish to grow their careers and want to become part of a technically strong and growth-oriented company focused on bringing innovative solutions to the difficult mission problems facing our customers. Description The Artificial Intelligence/Machine Learning (AI/ML) Engineer designs, creates, tests, and productizes AI/ML algorithms to solve business challenges. The AI/ML models they create should be capable of learning and making predictions as defined by the business logic developed to meet customer requirements. The AI/ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics application, interpretation, and presentation. The AI/ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models through iterative user and system feedback, the AI/ML Engineer designs and creates scalable solutions for optimal performance. The AI/ML Engineer may be responsible for leading geographically diverse teams and will often serve as a primary POC for AI-related matters, so must have exceptional analytical, problem-solving and communication skills. Expert knowledge of multiple programming languages, e.g. Python, Java, C, R, a plus. Select appropriate data sets Perform statistical analysis Run machine learning algorithms Use results to improve models Train and retrain systems when needed Experience in working with various ML libraries and packages Run standard test and evaluation protocols Provide system integration oversight Oversee Test and evaluation of AI and ML algorithms through an iterative design process to meet verification and validation requirements Research and implement a broad range of AI and ML algorithms and tools Design or Select appropriate data and knowledge representation methods Recognize software architecture, data modelling, and data structures Transform and convert data science prototypes into scalable solutions Verify data and model output quality Identify differences in data distribution that affect model performance