Technical AI / ML Engineer

Aleknagik Technology•Falls Church, VA

2d•Hybrid

About The Position

Aleknagik Technology, LLC is searching for a qualified candidate to fill the role of Technical AI / ML Engineer . This position supports enterprise-level data governance initiatives for a federal health organization by designing and operationalizing responsible AI and machine learning capabilities that enhance metadata management, data quality, lineage, classification, and decision support. The selected candidate must be within commuting distance of Falls Church, VA , as this role requires on-site and hybrid support in alignment with customer requirements. Duties The Technical AI / ML Engineer is responsible for designing, implementing, validating, and operationalizing AI/ML solutions that support data governance objectives while ensuring compliance with DoD, DHA, HIPAA, FISMA, RMF, and Zero Trust requirements. Duties include, but are not limited to: Design and deploy ML-assisted techniques to infer business meaning from technical metadata, including schema analysis and pattern recognition. Implement natural language processing (NLP) models to generate draft data dictionary entries, business glossary terms, and contextual metadata descriptions. Integrate AI-generated outputs into governed, human-in-the-loop workflows for data steward review and approval. Ensure AI-generated metadata is explainable, auditable, version-controlled, and traceable. Develop machine learning models to detect data quality issues such as anomalies, completeness gaps, schema drift, and degradation. Generate and integrate data quality indicators and trust scores aligned with VAULTIS "Trusted" principles. Support automated classification and compliance tagging for PII, PHI, CUI, and other sensitive data indicators. Align classification outputs with policy enforcement, access control, and audit mechanisms. Apply ML techniques to infer data lineage and semantic relationships where native lineage is incomplete. Validate inferred lineage and relationships through governance workflows prior to enterprise exposure. Design AI/ML workflows that operate in situ within DHA-approved environments, minimizing unauthorized data movement. Integrate AI services into enterprise catalogs, repositories, and federated platforms using APIs and modular architectures. Implement Responsible AI practices including transparency, explainability, bias monitoring, and human oversight. Document model purpose, training context, limitations, and validation results in accordance with DoD AI guidance. Support AI-enhanced self-service discovery capabilities such as semantic search and relevance ranking. Participate in user acceptance testing (UAT), usability evaluations, and adoption assessments. Analyze user feedback, telemetry, and performance metrics to refine models and improve effectiveness. Develop technical documentation, operational playbooks, and sustainment guidance. Support optional federated governance and AI-enabled data product lifecycle initiatives when exercised.

Requirements

4–7 years of experience in: Applied machine learning and natural language processing (NLP)
Data quality assessment, anomaly detection, and trust scoring techniques
Metadata management and enterprise data catalog platforms
Python, SQL, and modern ML frameworks
Responsible AI, model governance, and explainability practices
API integration and scalable deployment patterns
DoD, DHA, or federal security and compliance environments
Bachelor's degree in a related field, such as: Engineering Information Technology Data Science or Analytics Mathematics Physical or Life Sciences

Responsibilities

Design and deploy ML-assisted techniques to infer business meaning from technical metadata, including schema analysis and pattern recognition.
Implement natural language processing (NLP) models to generate draft data dictionary entries, business glossary terms, and contextual metadata descriptions.
Integrate AI-generated outputs into governed, human-in-the-loop workflows for data steward review and approval.
Ensure AI-generated metadata is explainable, auditable, version-controlled, and traceable.
Develop machine learning models to detect data quality issues such as anomalies, completeness gaps, schema drift, and degradation.
Generate and integrate data quality indicators and trust scores aligned with VAULTIS "Trusted" principles.
Support automated classification and compliance tagging for PII, PHI, CUI, and other sensitive data indicators.
Align classification outputs with policy enforcement, access control, and audit mechanisms.
Apply ML techniques to infer data lineage and semantic relationships where native lineage is incomplete.
Validate inferred lineage and relationships through governance workflows prior to enterprise exposure.
Design AI/ML workflows that operate in situ within DHA-approved environments, minimizing unauthorized data movement.
Integrate AI services into enterprise catalogs, repositories, and federated platforms using APIs and modular architectures.
Implement Responsible AI practices including transparency, explainability, bias monitoring, and human oversight.
Document model purpose, training context, limitations, and validation results in accordance with DoD AI guidance.
Support AI-enhanced self-service discovery capabilities such as semantic search and relevance ranking.
Participate in user acceptance testing (UAT), usability evaluations, and adoption assessments.
Analyze user feedback, telemetry, and performance metrics to refine models and improve effectiveness.
Develop technical documentation, operational playbooks, and sustainment guidance.
Support optional federated governance and AI-enabled data product lifecycle initiatives when exercised.