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.
