Artificial Intelligence/Machine Learning Engineer 3 - (Python, Java, Machine Learning)

Akina•Annapolis Junction, MD

6d•Onsite

About The Position

The Artificial Intelligence/Machine Learning (AI/ML) Engineer supports the VISTA project and related AI/ML work. Designs, creates, tests, and productizes AI/ML algorithms to solve business challenges. Creates AI/ML models capable of learning and making predictions as defined by the business logic developed to meet customer requirements. Is proficient in all aspects of model architecture, data pipeline interaction, and metrics application, interpretation, and presentation. Needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Designs and creates scalable solutions for optimal performance through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models through iterative user and system feedback. May be responsible for leading geographically diverse teams. Will often serve as a primary POC for AI-related matters. Expert knowledge of multiple programming languages, e.g. Python, Java, C, R, a plus. Demonstrated abilities in software engineering and AI/ML model test and evaluation. 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. Develop criteria for test and evaluation to include explainability and resiliency. Deep understanding of AI logic, semantics, ontologies, and knowledge representation. Demonstrated ability to design, evaluate, and productize AI/ML models on a range of commercial cloud-based architectures. Design ML algorithms according to customer requirements. Research, experiment with, and implement suitable ML algorithms and tools. Broaden current AIML frameworks and machine learning libraries.

Requirements

10 years experience deploying machine learning algorithms 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 is required.
7 additional years machine learning experience with a relevant Bachelor's degree may be substituted for a Master's degree.
Familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance.
Expert knowledge of multiple programming languages, e.g. Python, Java, C, R, a plus.
Demonstrated abilities in software engineering and AI/ML model test and evaluation.
Experience in working with various ML libraries and packages.
Deep understanding of AI logic, semantics, ontologies, and knowledge representation.
Demonstrated ability to design, evaluate, and productize AI/ML models on a range of commercial cloud-based architectures.

Nice To Haves

Expert knowledge of multiple programming languages, e.g. Python, Java, C, R, a plus.

Responsibilities

Designs, creates, tests, and productizes AI/ML algorithms to solve business challenges.
Creates AI/ML models capable of learning and making predictions as defined by the business logic developed to meet customer requirements.
Is proficient in all aspects of model architecture, data pipeline interaction, and metrics application, interpretation, and presentation.
Designs and creates scalable solutions for optimal performance through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models through iterative user and system feedback.
May be responsible for leading geographically diverse teams.
Will often serve 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 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.
Develop criteria for test and evaluation to include explainability and resiliency.
Design ML algorithms according to customer requirements.
Research, experiment with, and implement suitable ML algorithms and tools.
Broaden current AIML frameworks and machine learning libraries.