AI/ML Integration Specialist

Description

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. Worth being direct about scope: 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.

What You'll Get to Do as a AI/ML Integration Specialist:

• 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.

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.

Preferred Qualifications:

• 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.