Artificial Intelligence/Machine Learning Engineer 2 - (Python, TensorFlow, PyTorch)

Akina•Annapolis Junction, MD

19h•Onsite

About The Position

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.

Requirements

Expert knowledge of multiple programming languages, e.g. Python, Java, C, R.
Experience with standard machine language frameworks, e.g. Pytorch, TensorFlow.
Experience in working with various ML libraries and packages.
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.

Nice To Haves

Clearance: TS/SCI - Polygraph required

Responsibilities

Designs, creates, tests, and productizes AI/ML algorithms to solve business challenges.
Ensures AI/ML models are capable of learning and making predictions as defined by business logic.
Proficient in model architecture, data pipeline interaction, and metrics application, interpretation, and presentation.
Familiarity with application development, infrastructure management, data engineering, and data governance.
Designs and creates scalable solutions for optimal performance through understanding of training, retraining, deploying, scheduling, monitoring, and improving models.
May be responsible for leading geographically diverse teams.
Serves as a primary POC for AI-related matters.
Select appropriate data sets.
Perform statistical analysis.
Run machine learning algorithms.
Use results to improve models.
Train and retrain systems when needed.
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 modeling, 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.