AI/ML Engineer II

Reflexive Concepts•Annapolis Junction, MD

About The Position

Reflexive Concepts is seeking a skilled Artificial Intelligence/Machine Learning Engineer to join our team! Specifically, we are looking for an AI/ML Engineer to design, create, test, and productize AI/ML algorithms in support of the FFPP VISTA Development and Sustainment (MT&E Team) mission. The AI/ML Engineer should be proficient in model architecture, data pipeline interaction, and metrics application, and familiar with application development, infrastructure management, data engineering, and data governance.

Requirements

Five (5) years experience in applied machine learning in programs and contracts of similar scope, type, and complexity
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 learning frameworks, e.g. PyTorch, TensorFlow

Nice To Haves

Proficient in model architecture
Proficient in data pipeline interaction
Proficient in metrics application
Familiar with application development
Familiar with infrastructure management
Familiar with data engineering
Familiar with data governance

Responsibilities

Select appropriate data sets
Perform statistical analysis
Run machine learning algorithms
Use results to improve models
Train and retrain systems when needed
Experience 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