AI/Machine Learning Engineer

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.

Five (5) years experience in applied machine learning in programs and contracts of similar scope, type, and complexity is required. A Master's or Ph.D. degree in advanced math, artificial intelligence, data science, computer science or deep learning from an accredited college or university. 5 additional years of machine learning experience with a relevant Bachelor's degree may be substituted for a Master's degree. Experience with standard machine language frameworks, e.g. Pytorch, TensorFlow.
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