Junior AI/ML Engineer

Posted 11 Days Ago
Herndon, VA, USA
In-Office
Junior
Artificial Intelligence • Software
The Role
Supports machine learning development for fraud detection, including data preprocessing, feature engineering, model training, evaluation, experiment tracking, drift monitoring, and batch scoring. Maintains Python data pipelines, Jupyter reporting artifacts, and MLflow documentation. Assists with NLP/NER transformations and UiPath Maestro integration testing while following government security, data governance, CI/CD, and EPLC documentation requirements.
Summary Generated by Built In

Junior AI/ML Engineer

Location: Herndon, VA (Hybrid Work)

Preferred: US Citizenship

Node.Digital is an innovative solutions development company that combines agile development services with next-generation technologies in Cloud, Mobile, and AI/Machine Learning. We deliver state-of-the-art enterprise solutions to both government and commercial clients. We are looking for talented people to join our efforts to enable digitalization of organizations with AI Automation and Machine Learning.

 Key Responsibilities:

 • Support data preprocessing and feature engineering pipelines under senior engineer direction: clean, normalize, and validate fraud-related datasets; handle class imbalance preparation (SMOTE, undersampling) and train/validation/test split management.

• Assist in the development, training, and evaluation of supervised fraud classification models; compute and document standard evaluation metrics (accuracy, precision, recall, F1 score, AUC-ROC, confusion matrices) for government review in EPLC-required model evaluation reports.

• Maintain and monitor ML experiment tracking using MLflow or equivalent tooling approved for the IRMS environment; log hyperparameter configurations, training runs, and evaluation results with full reproducibility documentation.

• Support model drift detection and retraining pipelines: run scheduled evaluation jobs, flag performance degradation against established baselines, and escalate findings to the AI/ML Lead Engineer and Fraud AI/ML SME.

• Assist the NLP/NER pipeline team with data transformation tasks: format-convert NER pipeline outputs into feature-compatible schemas for downstream ML models; validate entity extraction quality against labeled reference sets.

• Develop and maintain Jupyter notebook-based model exploration and reporting artifacts for use in EPLC deliverables, sprint reviews, and government demonstrations.

• Support UiPath Maestro agent integration testing: prepare model inference payloads, validate agent input/output schemas, and assist with integration testing between ML model inference APIs and the persona-based agent layer.

• Implement and maintain data pipeline scripts (Python/Pandas/NumPy) for batch data ingestion, feature store updates, and model scoring batch runs within the IRMS security boundary.

• Follow and enforce IRMS boundary data handling procedures: ensure no PII/PHI is processed outside approved environments; maintain developer/test environment segregation per HHS security policy.

• Produce supporting artifacts for EPLC deliverables: training data specifications, model evaluation appendices, data dictionary updates, and sprint retrospective documentation as directed by the PM and AI/ML Lead.

• Participate in code reviews; adhere to OWASP secure coding standards, NIST SP 800-160 engineering principles, and Node’s internal CI/CD quality gates.


Requirements

Required Skills:

Bachelor’s degree in Computer Science, Data Science, Mathematics, Statistics, or a closely related field; recent graduates with strong applied ML coursework or project portfolios will be considered.

• 1–3 years of hands-on experience (including internships, graduate research, or project work) in machine learning, data science, or data engineering with Python.

• Proficiency in Python ML stack: scikit-learn, Pandas, NumPy; familiarity with at least one deep learning framework (TensorFlow or PyTorch) for model evaluation and inference tasks.

• Demonstrated experience with standard ML evaluation workflows: train/validation/test split design, cross-validation, metric computation, and results documentation.

• Experience with Jupyter notebooks for data exploration, model evaluation, and technical reporting.

• Familiarity with Git-based version control and CI/CD principles; ability to work within a structured sprint cadence with documented deliverable commitments.

• Demonstrated ability to handle sensitive data responsibly; understanding of data governance, access control, and the importance of environment segregation in a regulated or government setting.

• Strong written communication skills: ability to produce clear, organized technical documentation suitable for government review.


Benefits
  • Medical
  • Dental
  • Vision
  • Basic Life
  • Health Saving Account
  • 401K Matching
  • Three weeks of PTO/Sick
  • 11 Paid Holidays
  • Pre-Approved Online Training

Skills Required

  • Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, or a closely related field
  • 1–3 years of hands-on experience in machine learning, data science, or data engineering with Python, including internships, graduate research, or project work
  • Proficiency with Python, scikit-learn, Pandas, and NumPy
  • Familiarity with at least one deep learning framework, such as TensorFlow or PyTorch
  • Experience with train/validation/test splits, cross-validation, metric computation, and results documentation
  • Experience using Jupyter notebooks for data exploration, model evaluation, and technical reporting
  • Familiarity with Git-based version control and CI/CD principles
  • Ability to work within a structured sprint cadence with documented deliverable commitments
  • Ability to handle sensitive data responsibly and understand data governance, access control, and environment segregation in regulated or government settings
  • Strong written communication skills for producing organized technical documentation suitable for government review
  • US citizenship
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The Company
HQ: Leesburg, VA
11 Employees
Year Founded: 2017

What We Do

We Love Building Innovative Digital Solutions using Automation and A.I./ML to Solve Complex Problems for our Customer's Mission

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