AI/ML Engineer

AI/ML Specialist Location: Remote

Clearance Level: Active Secret

Overview Rackner is seeking an AI/ML Specialist to support the responsible design, development, evaluation, and deployment of artificial intelligence and automation capabilities across educational and operational environments.

The AI/ML Specialist will help identify high-value AI use cases, assess technical and organizational readiness, develop and deploy AI-enabled solutions, and support governance, monitoring, and adoption activities. This role will work across stakeholders, technical teams, data teams, and program leadership to integrate AI capabilities into existing business processes while ensuring solutions align with responsible AI principles, Government policy, and organizational strategy.

The AI/ML Specialist will also support the evaluation of emerging AI and large language model platforms, develop intelligent automation solutions, and contribute to AI governance, procurement readiness, and enterprise adoption.

Responsibilities

• Support the responsible integration of artificial intelligence into educational and operational business processes
• Design, develop, test, and deploy AI-driven solutions and process automations
• Conduct AI readiness assessments for proposed use cases
• Perform feasibility, benefit, risk, and implementation analyses using standardized evaluation frameworks
• Evaluate candidate AI use cases based on mission value, data readiness, technical complexity, security, governance, and expected return

• Develop technical supplements to enterprise AI governance frameworks, including:

• AI tool evaluation and adoption criteria
• Model performance monitoring and evaluation protocols
• AI risk and control requirements
• AI incident detection and response procedures

• Human oversight and escalation mechanisms

• Design and implement AI-enabled business process automations using Government-authorized platforms such as:

• Power Automate
• Power Apps
• Copilot Studio

• Comparable workflow and intelligent automation platforms

• Develop AI-enabled solutions through established solution delivery processes, including:

• Intake and requirements assessment
• Solution design
• Prototype development
• Testing and validation
• Security and governance review
• Production deployment

• Post-deployment monitoring

• Build and maintain AI agents and agent-enabled workflows
• Develop intelligent document processing capabilities for classification, extraction, summarization, routing, and related use cases
• Integrate AI capabilities with enterprise applications, data platforms, APIs, and workflow systems
• Develop and maintain automated workflows incorporating AI or machine learning components
• Support AI tool evaluation, pilot program development, and proof-of-concept initiatives
• Develop evaluation criteria, test plans, success metrics, and recommendations for AI pilots
• Evaluate emerging AI, machine learning, generative AI, and large language model platforms for organizational applicability
• Assess Department of Defense-developed, Government-provided, and commercially acquired AI/LLM platforms
• Support procurement readiness activities for AI tools, including requirements definition, technical evaluation, risk identification, and adoption criteria

• Develop AI-enabled analytics capabilities including:

• Predictive modeling
• Forecasting
• Natural-language query
• Classification and anomaly detection

• Decision-support capabilities

• Support development of semantic models and AI-ready data products within enterprise Lakehouse environments
• Develop Python-based AI/ML prototypes, integrations, evaluations, and automation components
• Support model and solution testing for accuracy, performance, reliability, bias, robustness, and operational suitability
• Establish and maintain model performance metrics and monitoring processes
• Support identification, triage, documentation, and response for AI-related incidents or unexpected model behaviors
• Collaborate with data engineers, analysts, governance teams, cybersecurity teams, and application developers to operationalize AI capabilities
• Translate stakeholder needs into AI use cases, technical requirements, solution designs, and acceptance criteria
• Support stakeholder engagement, demonstrations, workshops, training, and adoption activities
• Develop technical documentation, implementation guides, evaluation reports, and user-facin…