AI/ML Engineer (R-00194)

True Zero Technologies, a veteran-owned small business, was founded on the principle that the purposeful enablement of people and technology in an organization directly ties to the quality of its outcomes. True Zero recognizes that those outcomes begin and end with our people, and that is what we have built a community of like-minded, driven, and passionate individuals and innovators who are aligned in a common goal of delivering top-tier services to our customers. Our culture and commitment have been recognized through numerous accolades, including being named one of the Best Places to Work in 2023 in two categories (“Prosperous and Thriving” ($5MM–$50MM in gross revenue) and “Mid-Atlantic Region” (DC, DE, MD, NC, VA, WV)), and again in 2025 as a Best Places to Work honoree. In addition, True Zero earned coveted spots on the Inc. 5000 list of fastest-growing companies in America in 2022, 2023, and 2025 , a testament to our sustained growth driven by our people-first approach and unwavering dedication to excellence.

The AI/ML Engineer is responsible for designing, building, integrating, deploying, and operating artificial intelligence and machine learning capabilities that support mission and business workflows in secure government cloud environments. This role spans the AI/ML lifecycle, including data preparation, model and application development, evaluation, deployment, monitoring, and ongoing optimization.

The engineer will leverage FedRAMP-authorized services available within AWS GovCloud, while ensuring all AI/ML solutions align with applicable security, data handling, compliance, and Zero Trust requirements. The ideal candidate has strong Python development skills, experience with modern AI/ML architectures and data pipelines, and practical knowledge of deploying and evaluating models in cloud environments.

Job Responsibilities AI/ML Engineering & Mission Integration

• Build and integrate AI/ML capabilities supporting mission workflows using FedRAMP-authorized services available in AWS GovCloud, from data preparation through deployment, evaluation, and monitoring.
• Design, develop, test, and operationalize AI/ML solutions aligned to defined mission and business requirements.
• Translate operational use cases into scalable AI/ML architectures, services, and integration patterns.
• Develop APIs, services, and application integrations that expose AI/ML capabilities to mission applications and enterprise platforms.
• Collaborate with application engineers, data engineers, cloud engineers, cybersecurity teams, and mission stakeholders throughout the solution lifecycle.
• Conduct technical evaluations and proof-of-concept implementations to determine whether proposed AI/ML technologies are appropriate for the mission, security environment, and available GovCloud services.
• Maintain technical documentation covering architecture, model behavior, interfaces, dependencies, deployment procedures, and operational requirements.

Python & AI/ML Development

• Develop production-quality AI/ML applications and supporting services using Python.
• Build reusable Python modules, services, utilities, and automation supporting data processing, inference, evaluation, and system integration.
• Apply standard software engineering practices including source control, automated testing, code review, dependency management, and CI/CD.
• Develop and integrate machine learning models, foundation models, or AI services based on approved use cases and architecture.
• Optimize AI/ML application performance, reliability, scalability, and resource utilization.
• Troubleshoot issues involving model behavior, data quality, application integration, cloud services, and runtime environments.

Generative AI & Retrieval-Augmented Generation

• Design and implement Generative AI and Retrieval-Augmented Generation (RAG) patterns when included within the approved solution scope.
• Develop workflows for document ingestion, parsing, chunking, embedding generation, indexing, retrieval, prompt construction, and model inference.
• Integrate approved large language models and foundation-model services with enterprise applications and mission data sources.
• Evaluate retrieval quality, response relevance, groundedness, hallucination risk, and overall solution effectiveness.
• Develop prompt-management, model-routing, and orchestration patterns where appropriate.
• Implement safeguards and validation m…