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Data Engineer – AI & Machine Learning

Job in Tysons, Fairfax County, Virginia, USA
Listing for: Integral Federal, Inc.
Full Time position
Listed on 2026-11-01
Job specializations:
  • IT/Tech
    Data Engineering, AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 110000 - 150000 USD Yearly USD 110000.00 150000.00 YEAR
Job Description & How to Apply Below
Overview

Data Engineer – AI & Machine Learning

Location: McLean, VA (Headquarters) – hybrid schedule with 3 days in the office

Job Type: Full-Time / Primarily Internal Core Team (Occasional Billable Client Support)

Clearance Requirement: Ability to obtain and maintain a DoD Secret Clearance (Active Secret or higher preferred

About Us

We are a premier, mid-sized federal government contractor committed to advancing national security. We specialize in delivering mission-critical technology solutions to the Department of War (DoW), the Intelligence Community (IC), and security-focused Federal Civilian agencies (e.g., DHS, TSA). We have an agile, innovative culture where our technical experts are empowered to solve the government's most complex challenges.

Position Overview

We seek an innovative and highly skilled Data Engineer for AI and Machine Learning to join our core technical group. This position sits within our internal Office of Technology, driving internal projects, building cutting-edge capabilities, and establishing best practices. You will interface with government stakeholders and may flex onto billable client projects as needed.

In this role, you will be the data architectural backbone of our AI and ML initiatives. You will design data architectures from the ground up and build complex pipelines for structured and unstructured multimodal data.

Responsibilities
  • Data Architecture & Strategy: Design, build, and optimize scalable data architectures capable of supporting advanced ML models, analytics, and high-volume data processing for federal mission sets.
  • Pipeline Engineering (ETL/ELT): Develop, construct, test, and maintain resilient data pipelines, including those for synthetic data. Process and integrate
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