AI/Machine Learning Engineer SME

Integration Innovation, Inc. (i3)•Washington, DC

1d•$200,000 - $350,000•Hybrid

About The Position

Integration Innovation, Inc. (i3) is seeking a motivated SME level AI/Machine Learning Engineer to support the Defense Intelligence Agency (DIA) Enterprise Data Management & Analytics (EDMA) effort. As part of this dynamic program, you will architect enterprise-ready AI/ML solutions and serve as primary technical interface with government stakeholders. This role is ideal for experienced engineers who can champion responsible-AI and governance principles, ensuring approaches align with IC and DoD directives. The AI Team is distributed across multiple projects and could have potential for some hybrid work.

Requirements

U.S. Citizenship.
Active Secret clearance w/ the ability to obtain and maintain a TS/SCI security clearance.
Bachelor’s degree (Master’s preferred) in Computer Science, Engineering, Applied Mathematics, Data Science, or related field.
Minimum 14 years of progressive experience in AI/ML, data engineering, or advanced analytics.
Demonstrated experience architecting and deploying enterprise AI/ML systems.
Deep proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX).
Experience leading technical teams, performing architecture trade studies, and guiding full ML lifecycle development.
Strong understanding of secure software engineering, distributed systems, DevSecOps, and containerization.
Excellent communication skills and ability to interface with senior government stakeholders.

Nice To Haves

Experience supporting AI/ML projects in government, defense, or intelligence environments.
Familiarity with Intelligence Community mission needs and data types.
Exposure to cloud‑native development (AWS, Azure, GovCloud).
Experience building or supporting NLP, computer‑vision, generative‑AI, or anomaly‑detection models.
Knowledge of MLOps, CI/CD, container orchestration, or distributed training frameworks.

Responsibilities

Architect enterprise‑ready AI/ML solutions, including model‑training pipelines, inference services, feature stores, data‑ingestion pathways, and MLOps frameworks aligned to requirements.
Define the technical roadmap, breaking down complex mission needs into actionable architectures, workstreams, and implementation plans.
Lead multi‑disciplinary engineering teams, providing direction on algorithms, model selection, data strategy, integration, and delivery cadence.
Oversee sprint planning and technical execution, ensuring deliverables are on time, blockers are removed, and engineering artifacts meet enterprise standards.
Serve as primary technical interface with government stakeholders, articulating architecture decisions, model behaviors, risk tradeoffs, and recommended solution pathways.
Establish and enforce best practices across ML engineering, DevSecOps, coding standards, test automation, and model life‑cycle governance.
Conduct technical reviews and mentor engineers, elevating team capability and ensuring alignment with mission outcomes.
Guide evaluation and adoption of advanced ML tools, including deep‑learning frameworks (PyTorch, TensorFlow, JAX), distributed compute, MLOps platforms, generative‑AI modernization patterns, and enterprise scale data solutions.
Lead integration of AI capabilities into cloud‑native or hybrid architectures, ensuring compliance with security, accreditation, and enterprise‑data‑management requirements.
Provide SME‑level support for strategic planning, including solution briefs, architecture diagrams, CONOPS development, and decision‑ready artifacts for government review panels.
Champion responsible‑AI and governance principles, ensuring approaches align with IC and DoD directives.