AI/ML Engineer Level 2

Markon

12d•$210,000 - $225,000

About The Position

Markon is seeking an experienced AI / Machine Learning Engineer to design, develop, evaluate, and transition advanced AI and ML solutions into scalable, operational systems for our Fort Meade Customer. This role focuses on applying statistical analysis and machine learning techniques, overseeing testing and evaluation, and ensuring model performance, reliability, and integration within mission-focused environments.

Requirements

Active TS/SCI w/ Polygraph with this Customer.
Master’s degree or PhD with 5+ years of relevant experience in AI, machine learning, or data science.
Hands-on experience with machine learning frameworks such as PyTorch and TensorFlow.
Strong background in statistical analysis and machine learning algorithm development.
Experience working with a variety of ML libraries and packages.
Demonstrated experience supporting test, evaluation, verification, and validation (TEVV) of AI/ML systems.
Ability to transition models from research or prototype environments into scalable production systems.

Nice To Haves

Experience integrating AI/ML solutions into mission or enterprise systems.
Familiarity with model monitoring, drift detection, and performance degradation analysis.
Experience working in classified, regulated, or high-reliability environments.

Responsibilities

Research, design, and implement a broad range of AI and machine learning algorithms and tools.
Select and curate appropriate data sets and data representations to support model development.
Perform statistical analysis to inform model design and performance evaluation.
Develop, train, retrain, and optimize machine learning models using frameworks such as PyTorch and TensorFlow.
Execute test and evaluation protocols to verify and validate AI/ML algorithms through iterative design cycles.
Use evaluation results to refine models and improve accuracy, robustness, and performance.
Provide system integration oversight to ensure AI/ML solutions transition successfully into production environments.
Transform data science prototypes into scalable, maintainable, and operational solutions.
Verify data integrity, model outputs, and performance quality.
Identify and address data distribution shifts and other factors that impact model performance over time.
Apply knowledge of software architecture, data modeling, and data structures to support enterprise integration.