Data Engineer (AI) Subject Matter Expert (SME) (4799) (TS/SCI) (Ft. Belvoir, VA - Nolan Bldg)

SMX•Fort Belvoir, VA

46d•Onsite

About The Position

SMX is seeking a Data Engineer (AI) Subject Matter Expert (SME) to spearhead the development and sustainment of data pipelines and data services that enable AI/ML capabilities within Army Intelligence mission environments. This role defines standards, develops and implements end-to-end data pipeline strategy, and guides a small team to deliver reliable, secure, and observable data services for training, inference, and analytics. The Data Engineer (AI) SME partners with AI/ML engineers, analysts, platform teams, and mission leadership to align data capabilities with operational outcomes, security, and governance. This is a full-time onsite position in Ft. Belvoir, VA.

Requirements

Active TS security clearance and eligible for SCI and NATO read-on prior to starting work
Meet all requirements to receive a privileged user account on a TS/SCI information system (e.g. Army Cloud Computing Service Provider) prior to starting work. The requirements are currently defined in DoDD 8140.01.
Security+ or related DoDD 8140-relevant certification.
Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
7+ years of experience in data engineering or large-scale data processing in an AI/ML-supporting capacity; 2+ years leading data engineering efforts (tech lead, team lead, or equivalent).
Proven experience designing and operating ELT/ETL at scale; orchestration.
Highly proficient with programming and/or scripting languages (e.g., Python, SQL, Bash).
Proficient with cloud platforms, data storage systems, and workflow orchestration tools.
Proficient with containerization and CI/CD principles.
Knowledgeable with military operations, intelligence workflows, and digital platforms.
Ability to work within the constraints of military security and compliance standards.
Highly proficient in working closely with model developers, analysis, and platform engineers to understand data requirements and operational context.
Expert problem-definition and solution-development skills to troubleshoot and optimize data operations in support of AI/ML systems.
Excellent communication and interpersonal skills for effective collaboration with diverse teams.
Support new team member onboarding and assist with training materials as needed.

Nice To Haves

Background in data privacy, ethical AI deployment, or regulated industries
AWS Certified Security – Specialty or equivalent cloud certifications.

Responsibilities

Translate mission needs into a data engineering roadmap, delivery plans, and measurable outcomes for AI/ML use cases.
Serve as primary technical point of contact for data dependencies across model development, analytics, and platform teams; drive cross-team decision making and risk trade-offs.
Review and recommend designs, requests, and deployments from team members; develop quality standards and coding conventions.
Define reference architectures and patterns for ingestion, ELT/ETL, lake/lakehouse/warehouse integration, and feature data delivery.
Select and standardize pipeline tooling and storage/layout strategies to balance performance, cost, and security.
Establish schema, partitioning, and lifecycle strategies to support model retraining, reproducibility, and rollback.
Design and oversee ETL/ELT pipelines that supply structured and unstructured data to AI/ML workflows.
Establish and ensure compliance with SLOs/SKIs for data freshness, latency, and completeness; oversee and implement autoscaling and performance tuning across cloud/hybrid environments.
Implement and oversee data ingestion, cleansing, transformation, normalization, and feature preparation steps according to established methods.
Ensure pipelines are reliable, repeatable, and well-documented.
Perform data profiling, validation, and integrity checks to ensure high-quality inputs to AI/ML models.
Monitor data pipeline performance and data flow health; document and escalate issues to senior engineers.
Drive issue triage and root-cause analysis; lead post-incident reviews and preventative improvements.
Contribute to improving data observability and traceability.
Lead employment of Agile methodologies for project management.
Manage deployment pipelines, containerization, and CI/CD workflows for AI services in the cloud to ensure reproducible data operations.
Implement and oversee robust monitoring, observability, and performance tracking for AI-supporting data systems.
Provide training and technical support to military personnel to ensure effective adoption and utilization of AI technologies.
Ensure comprehensive documentation of integration architecture, processes, and operational dependencies, to include data structures, transformation logic, data lineage, pipeline workflows, and operational procedures.
Mentor and coach engineers; build repeatable playbooks and raise the team’s technical maturity.
Ensure compliance with military security, privacy and governance standards for AI-supporting data systems.
Ensure processes and procedures leverage changes in data engineering and AI/ML governance best practices.