AI Engineer

Job Family:

Data Science Consulting

Travel Required:

None

Clearance Required:

Active Secret

What You Will Do:

• Implement AI strategy to increase adoption of AI technologies across client organization.
• Coordinate with related stakeholders to establish on-premises AI server.
• Integrate AI capabilities (prompt orchestration, tool/function calling, embeddings, fine-tuning) into applications and services.
• Collaborate with data scientists, platform engineers, and product teams to iterate on use cases, deliver POCs/MVPs, and harden them for scale.
• Follow responsible AI practices and security/compliance requirements across commercial and public sector environments.
• Conduct digital transformation initiatives leveraging AI technologies to improve organization’s mission accomplishment and increase operating efficiencies.

What You Will Need:

• An ACTIVE and MAINTAINED "SECRET" Federal or DoD security clearance
• Bachelor’s degree
• EIGHT (8) or more years of professional experience including the following:
• THREE (3) years of experience in software, data, RAG architecture/vector databases or ML engineering, including building and operating cloud-native services including but not limited to OpenAI, Anthropic, Google Gemini, or AWS Bedrock
• TWO (2) years focused specifically on Generative AI/LLM technologies.
• ONE (1) year optimizing for token efficiency and cost management in production LLM applications (e.g., prompt compression, caching, context window management, model routing).

What Would Be Nice To Have:

• Ability to write clear documentation and collaborate in cross-functional teams.
• Experience contributing to client-facing engineering in consulting or product environments.
• Master’s degree in relevant technical or engineering program.
• Demonstrated experience deploying and managing on-premises, self-hosted model infrastructure (not solely cloud-based/SaaS AI tools).
• Experience architecting scalable, hybrid (on-prem + cloud) AI/ML solutions end-to-end, from data pipeline to deployment.
• Working knowledge of AI security concepts, including adversarial robustness, model access controls, and data protection in restricted environments.
• Demonstrates expert experience in leading development efforts with clear examples of value added to the client.
• Demonstrates experience interacting with end-users to understand and document business and functional requirements for complex projects, as well as collaborating with technical teams across the full software development life cycle.
• Demonstrates considerable proven knowledge of organizational planning and project management specific to lifecycle development processes.