Junior AI/ML Engineer

Node.Digital•Herndon, VA

3d•Hybrid

About The Position

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.

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 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.

Nice To Haves

US Citizenship

Responsibilities

Support data preprocessing and feature engineering pipelines under senior engineer direction: clean, normalize, and validate HRSA 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 (Rohit) 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.