Hybrid Junior AI/ML Engineer - Gov-Ready Fraud Models

Node.Digital LLC; Herndon (VA); Hybrid; USD 80,000 - 110,000; Full time

Benefits offered by this job

Medical

Dental

Vision

Basic Life

HSA

401K Matching

PTO/Sick & Holidays

Pre-Approved Online Training

Job summary

Node.Digital LLC in Herndon, VA, is seeking a Junior AI/ML Engineer to join our AI/ML initiatives for government and commercial clients. You will support data preprocessing, feature engineering, and model development under senior guidance, using Python, scikit-learn, Pandas, and DL frameworks.

You will document evaluation metrics, track experiments, and help ensure secure data handling within IRMS boundaries. Strong communication and collaboration with a cross‑functional team are essential.

Qualifications

• 1–3 years of hands-on ML experience with Python.
• Proficiency in Python ML stack and at least one DL framework.
• Experience with ML evaluation workflows and model reporting.
• Experience with data governance and environment segregation.

Responsibilities

• Support data preprocessing and feature engineering pipelines under supervision.
• Develop, train, and evaluate ML models; document metrics for review.
• Maintain ML experiment tracking and reproducibility records.
• Assist NLP/NER data transformation to feature schemas.
• Maintain notebooks for exploration and government demos.
• Assist with CI/CD and secure coding practices.
• Follow data handling procedures within IRMS boundaries.

Skills

Python

ML basics

Jupyter notebooks

Documentation

Git/CI-CD

Data governance

Written communication

Education

Bachelor's degree in CS/Data Science/Math/Stats

Tools

scikit-learn

Pandas

NumPy

TensorFlow

PyTorch

Git

CI/CD

Job description

Node.Digital LLC in Herndon, VA, is seeking a Junior AI/ML Engineer to join our AI/ML initiatives for government and commercial clients. You will support data preprocessing, feature engineering, and model development under senior guidance, using Python, scikit-learn, Pandas, and DL frameworks.

You will document evaluation metrics, track experiments, and help ensure secure data handling within IRMS boundaries. Strong communication and collaboration with a cross‑functional team are essential.