Junior AI/ML Engineer

Node.Digital•Herndon, VA

4d•Hybrid

About The Position

Node.Digital is seeking a Junior AI/ML Engineer to support AI automation and machine learning work for government and commercial clients. The role is based in Herndon, Virginia, with a hybrid work arrangement, and US citizenship is preferred. Working under senior engineer direction, the engineer will contribute to fraud-related data preparation, supervised model development and evaluation, experiment tracking, drift monitoring, NLP/NER data integration, and batch data pipelines. The position also supports UiPath Maestro agent integration testing and prepares technical documentation and artifacts for government review and project deliverables. Work must comply with IRMS security-boundary procedures, HHS security policy, OWASP secure coding standards, NIST SP 800-160 engineering principles, and Node’s internal CI/CD quality gates.

Requirements

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 machine learning, data science, or data engineering experience with Python; internships, graduate research, and project work count.
Proficiency with the Python ML stack, including scikit-learn, Pandas, and NumPy.
Familiarity with at least one deep learning framework, TensorFlow or PyTorch, for model evaluation and inference tasks.
Experience with standard ML evaluation workflows, including train/validation/test split design, 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, and ability to work within a structured sprint cadence with documented deliverable commitments.
Ability to handle sensitive data responsibly and understanding of data governance, access control, and environment segregation in regulated or government settings.
Strong written communication skills and ability to produce clear, organized technical documentation suitable for government review.

Nice To Haves

US citizenship is preferred.

Responsibilities

Support data preprocessing and feature engineering, including cleaning, normalizing, validating fraud-related datasets, preparing for class imbalance, and managing train/validation/test splits.
Assist with developing, training, and evaluating supervised fraud classification models; compute and document evaluation metrics for government review and EPLC model evaluation reports.
Maintain ML experiment tracking using MLflow or an approved equivalent, documenting hyperparameters, training runs, evaluation results, and reproducibility information.
Support model drift detection and retraining pipelines by running scheduled evaluation jobs, flagging performance degradation against baselines, and escalating findings to the AI/ML Lead Engineer and Fraud AI/ML SME.
Assist the NLP/NER pipeline team by converting pipeline outputs into feature-compatible schemas and validating entity extraction quality against labeled reference sets.
Develop and maintain Jupyter notebook-based model exploration and reporting artifacts for EPLC deliverables, sprint reviews, and government demonstrations.
Support UiPath Maestro agent integration testing by preparing inference payloads, validating input/output schemas, and assisting with tests between ML inference APIs and the persona-based agent layer.
Implement and maintain Python/Pandas/NumPy scripts for batch data ingestion, feature store updates, and batch model scoring within the IRMS security boundary.
Follow IRMS data-handling procedures, ensuring PII/PHI is not processed outside approved environments and maintaining developer/test environment segregation under HHS security policy.
Produce supporting EPLC artifacts, including 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 and follow OWASP secure coding standards, NIST SP 800-160 engineering principles, and Node’s internal CI/CD quality gates.