AI/ML Integration Specialist - AFTAS Pro

OneGlobe•Washington, DC

12d

About The Position

We are hiring an AI/ML Integration Specialist to provide technical direction for bringing approved AI capabilities into an existing federal acquisition enterprise — its toolset, its data, and its accredited infrastructure. This is an integration role, not a model development role. The hard problems here are schema alignment, API and connector configuration, data readiness, and building the verification and monitoring frameworks that let a security-conscious customer trust an AI-driven output. If you want to train foundation models, this is not that job. If you want to make AI actually work inside a large regulated enterprise, it is.

Requirements

5+ years integrating AI and machine learning capabilities into enterprise environments, with at least some of that work inside DoD or federal frameworks.
Master's degree in computer science or data science, or a bachelor's degree with equivalent additional experience.
Active Secret clearance (interim Secret considered with approval).
Hands-on API and data connector configuration, schema mapping, and data pipeline engineering.
Ability to explain AI capability, limitation, and risk to non-technical senior stakeholders without overselling either.

Nice To Haves

WS Certified Machine Learning Engineer – Associate, or AWS Certified AI Practitioner.
Holders of the retired AWS Certified Machine Learning – Specialty are equally welcome — that credential remains active for three years from the date earned.
Google Cloud Professional Machine Learning Engineer, or comparable current cloud ML credential.
Experience with AI test and evaluation, continuous verification, or security monitoring in an accredited government environment.
Prompt engineering and AI-enabled workflow design experience.
Familiarity with federal AI governance and compliance expectations.

Responsibilities

Evaluate existing and emerging AI tools for integration suitability against real mission requirements.
Analyze current system architectures to determine where AI integration produces value and where it introduces unacceptable risk.
Draft technical roadmaps supporting phased AI adoption, sequenced by dependency and risk rather than by novelty.
Identify and document integration risks, interoperability challenges, and resource requirements.
Define data mapping and schema alignment strategies between source systems and AI tools.
Provide design support for modifying data pipelines to meet AI requirements.
Draft data quality, security, and compliance procedures that make enterprise data fit for AI consumption.
Configure APIs and data connectors for AI services, working alongside platform and security teams.
Document and define technical requirements for interfacing with AI models.
Validate that integrated AI solutions actually match how users work, not just how the architecture diagram says they should.
Design and document continuous verification frameworks that keep AI components secure, usable, and performing within established parameters.
Develop test and evaluation criteria the government can use to validate that integrated AI systems meet mission objectives.
Conduct ongoing technical monitoring and vulnerability assessment, verifying the accuracy, safety, and reliability of AI-driven outputs.