AI Security Engineer - Mid

Koniag Data Solutions, a Koniag Government Services company, is seeking an experienced AI Security Engineer (Mid) to support a comprehensive enterprise cybersecurity services program for a federal government client. This position requires the ability to obtain and maintain a Minimum Background Investigation (MBI) or higher, PIV credentials, and all requisite IT access authorizations prior to performing work. Primary work will be performed at the client site in Washington DC and approved remote/telework locations.

We offer competitive compensation and an extraordinary benefits package including health, dental and vision insurance, 401K with company matching, flexible spending accounts, paid holidays, three weeks paid time off, and more.

This role serves as a key technical contributor responsible for supporting the design, implementation, integration, and governance of Artificial Intelligence (AI) and machine learning capabilities within the client's enterprise cybersecurity ecosystem-including AI-powered threat detection, automated compliance monitoring, machine learning-driven risk assessment, and AI-enhanced Security Information and Event Management (SIEM) capabilities-in alignment with applicable federal AI governance frameworks, NIST guidelines, and agency cybersecurity policies.

The ideal candidate is a technically proficient AI security professional with demonstrated hands-on experience developing and integrating AI and machine learning solutions within complex federal IT environments. This individual must possess solid expertise in AI security engineering, federal cybersecurity frameworks, and the practical application of AI and machine learning technologies to strengthen enterprise cybersecurity operations, threat detection, incident response, and compliance automation capabilities under the direction of the AI Security Engineer Lead.

The AI Security Engineer (Mid) will serve as a key technical contributor within the program's AI security engineering function, working under the direction of the AI Security Engineer Lead to design, develop, implement, test, and maintain AI-powered cybersecurity capabilities across the client's enterprise environment. This individual is responsible for supporting the full lifecycle of AI security engineering activities-from requirements analysis and solution design through development, integration, testing, deployment, and ongoing optimization-ensuring all AI capabilities are secure, governed, compliant, and effectively integrated into operational cybersecurity workflows.

Principal responsibilities will include but are not limited to:

AI Security Engineering & Implementation

• Support the design, development, testing, deployment, and maintenance of AI-powered security solutions for real-time threat detection, automated incident response, behavioral analytics, and compliance monitoring within the client's enterprise cybersecurity environment.
• Develop and maintain machine learning models for anomaly detection, predictive threat analytics, and automated threat hunting, working collaboratively with the AI Security Engineer Lead and SOC analysts to ensure model outputs are operationally relevant and effectively integrated into SOC workflows.
• Implement and maintain AI-driven SIEM enhancements within platforms such as Microsoft Sentinel, including development of machine learning-based detection rules, behavioral analytics models, User and Entity Behavior Analytics (UEBA) configurations, and automated response playbooks to improve incident detection accuracy and accelerate triage activities.
• Support the integration of AI capabilities into the enterprise cybersecurity tool stack, including SIEM, Endpoint Detection and Response (EDR), threat intelligence platforms, vulnerability management systems, and SOC operational workflows, ensuring seamless data flows, accurate model inputs, and reliable automated outputs.
• Automate cybersecurity workflows using AI and scripting technologies to improve the efficiency and speed of security incident response, vulnerability prioritization, compliance assessment, and risk management activities across the enterprise.
• Develop and implement automated compliance monitoring tools leveraging AI to continuously assess adherence to NIST SP 800-53 controls, FISMA requirements, and agency-specific security standards, reducing manual assessment burden and enhancing continuous monitoring effectiveness.
• Support the implementation of AI-driven risk assessment methodologies, developing automated data pipelines, scoring models, and visualization capabilities that provide actionable risk intelligence to cybersecurity leadership and Government stakeholders.
• Assist in the implementation of AI capabilities for fraud detection, policy enforcement, and risk mitigation across cybersecurity operations, developing and tuning automated detection algorithms and behavioral models aligned with agency requirements.
• Support the enhancement of regulatory reporting capabilities by leveraging AI to analyze compliance data, identify trends, and generate automated reports supporting FISMA, FITARA, and other federal reporting requirements.
• Assist in the secure integration of Perplexity and related AI tools within the agency enterprise environment, supporting configuration, access control implementation, data handling governance, and compliance verification activities.

AI Model Development & Optimization

• Develop, train, evaluate, and operationalize machine learning models for cybersecurity use cases, following rigorous model development practices including data preprocessing, feature engineering, model selection, hyperparameter tuning, cross-validation, and performance evaluation.
• Implement and maintain CI/CD pipelines for automated AI model updates, security enhancements, and performance monitoring, ensuring models remain accurate, effective, and aligned with the evolving threat landscape throughout the period of performance.
• Monitor deployed AI model performance on an ongoing basis, detecting and remediating model drift, accuracy degradation, false positive/negative rate changes, and adversarial manipulation risks that could reduce the effectiveness of AI-powered security capabilities.
• Identify and implement algorithmic optimizations to improve the computational efficiency, resource utilization, and detection accuracy of deployed AI and machine learning models within the enterprise environment.
• Conduct regular testing and validation of AI model outputs, collaborating with SOC analysts and security engineers to verify that model predictions and automated decisions align with operational security requirements and acceptable risk thresholds.
• Document all model development activities, including data sources, preprocessing steps, model architectures, training parameters, evaluation metrics, and deployment configurations, maintaining comprehensive model documentation in the program's designated knowledge management repository.

AI Governance & Compliance Support

• Support the implementation and maintenance of the program's AI governance framework, assisting the AI Security Engineer Lead in assessing AI tools, models, and capabilities for security, privacy, and compliance risks prior to deployment and throughout their operational lifecycle.
• Conduct AI risk assessments for proposed and existing AI integrations, aligning assessments with NIST AI RMF guidelines and documenting identified risks and mitigation strategies in the enterprise risk register and applicable system security documentation.
• Assist in ensuring all AI capabilities and integrations comply with applicable federal privacy laws, Executive Orders on AI, OMB AI governance policies, agency AI policies, and records management requirements, coordinating with privacy and compliance teams on cross-cutting requirements.
• Support the development and maintenance of AI-specific security documentation, including AI risk assessments, AI model inventories, AI system security plan inputs, and AI incident response procedure documentation, ensuring all documentation meets applicable federal standards and agency template requirements.
• Assist in integrating AI risk management activities into the broader enterprise RMF and FISMA compliance programs, ensuring AI-related risks and controls are accurately reflected in system security plans, POA&Ms, and continuous monitoring reporting.
• Support supply chain risk assessments for AI tools, third-party AI models, and external AI data sources, documenting findings and recommendations in accordance with applicable federal supply chain risk management requirements.

Technical Collaboration & Documentation

• Collaborate closely with the AI Security Engineer Lead, SOC Cybersecurity Operations Technical Lead, Cybersecurity Architect, security engineering teams, and other functional leads to ensure AI capabilities are effectively integrated into operational workflows, security architectures, and engineering processes across all program functional areas.
• Participate in technical working sessions, architecture reviews, and sprint planning activities, contributing AI security engineering expertise and providing accurate technical input to support program planning and delivery activities.
• Develop and maintain technical documentation for all AI security capabilities assigned, including system descriptions, architecture diagrams, data flow diagrams, model documentation, operational runbooks, and standard operating procedures, ensuring documentation is current, accurate, and maintained in the program's designated repository.
• Prepare and contribute to AI-related program deliverables, including status reports, recommendation documents, project plans, hardware and software review reports, and assessment reports, in accordance with required timelines and Government quality standards.
• Provide technical support and expertise to ISSO and Security Control Assessor (SCA) personnel in the development of security documentation supporting AI system FISMA activities, including SSP inputs, control implementation descriptions, and assessment evidence.
• Support the development and delivery of AI security awareness briefings and training materials for Government stakeholders and agency personnel under the direction of the AI Security Engineer Lead.

Education and Experience:

Required:

• Bachelor's degree in computer science, Artificial Intelligence, Cybersecurity, Information Systems, Data Science, or a related field from an accredited college or university.
• Minimum of 4 years of experience in information technology or cybersecurity, with at least 2 years of demonstrated hands-on experience in AI security engineering, machine learning development, or AI governance within a complex enterprise IT environment.
• Demonstrated experience developing, training, and deploying machine learning models for security or data analytics applications, including anomaly detection, behavioral analytics, predictive modeling, or classification use cases.
• Experience integrating AI or machine learning capabilities into enterprise security tools or operational workflows.
• Familiarity with federal cybersecurity frameworks and standards, including FISMA, NIST SP 800-53, and applicable AI governance guidelines.
• Ability to obtain and maintain a Minimum Background Investigation (MBI) or higher, PIV credentials, and all requisite IT access authorizations; must be eligible for Top Secret clearance access should such a requirement arise during the period of performance.

Preferred:

• Master's degree in Artificial Intelligence, Machine Learning, Cybersecurity, Computer Science, Data Science, or a related field.
• Prior experience supporting federal civilian agency AI security or cybersecurity programs in an engineering or technical contributor capacity.
• Experience working on GSA Multiple Award Schedule (MAS) HACS SIN contracts or comparable federal IT cybersecurity contract vehicles.
• Familiarity with the NIST AI Risk Management Framework (AI RMF) and its practical application within a federal agency cybersecurity program.

Required Skills and Competencies:

• Strong communication skills in English-both written and oral-with the demonstrated ability to clearly explain AI security engineering concepts, model development findings, and technical recommendations to both technical peers and non-technical Government stakeholders.
• Solid hands-on technical expertise in AI and machine learning technologies, including supervised and unsupervised learning, neural networks, natural language processing, anomaly detection algorithms, and predictive analytics, with demonstrated ability to apply these technologies to enterprise cybersecurity use cases.
• Proficiency with AI and machine learning development frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, or equivalent) for developing, training, evaluating, and operationalizing machine learning models for cybersecurity applications.
• Proficiency with scripting and automation languages, including Python, PowerShell, SQL, and JSON, for AI model development, data pipeline construction, feature engineering, and cybersecurity workflow automation.
• Experience with AI-driven SIEM capabilities, including the development of machine learning-based detection rules, behavioral analytics models, and automated response playbooks within enterprise SIEM platforms such as Microsoft Sentinel or equivalent solutions.
• Experience developing and maintaining CI/CD pipelines for automated AI model updates and performance monitoring within a DevSecOps delivery environment.
• Familiarity with AI governance principles, including AI risk assessment, AI model inventory management, AI lifecycle management, and responsible AI practices aligned with NIST AI RMF or equivalent federal AI governance standards.
• Knowledge of federal cybersecurity frameworks and standards, including FISMA, NIST SP 800-53, NIST SP 800-207 Zero Trust Architecture, OMB M-22-09, and FedRAMP, as they relate to AI security engineering and governance responsibilities.
• Familiarity with enterprise cybersecurity functional areas including SOC operations, incident response, threat intelligence, vulnerability management, and security engineering, sufficient to effectively develop and integrate AI capabilities into operational cybersecurity workflows.
• Experience developing and maintaining technical documentation for AI systems and cybersecurity capabilities, including architecture diagrams, data flow diagrams, model documentation, and standard operating procedures meeting applicable federal standards.
• Strong analytical and problem-solving skills with the ability to diagnose AI model performance issues, integration failures, and data quality problems, and develop effective corrective actions under the direction of the AI Security Engineer Lead.
• Familiarity with privacy and data protection requirements applicable to AI systems, including Privacy Act considerations, data minimization principles, and privacy risk assessment processes for AI-enabled capabilities.
• Experience assessing supply chain risks associated with third-party AI tools, models, and data sources within a federal IT environment.

Desired Skills and Competencies:

• CompTIA Security+, Certified Ethical Hacker (CEH), Certified Information Systems Security Professional (CISSP), or equivalent cybersecurity certification.
• AI or machine learning professional certification (e.g., AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate, or equivalent).
• Experience with Microsoft Sentinel AI and machine learning capabilities, including UEBA configuration, fusion detection rules, and automated response playbook development within a federal Azure Government environment.
• Experience with AWS AI and machine learning security services (e.g., Amazon GuardDuty ML, Amazon Macie, AWS Security Hub) within a FedRAMP-compliant cloud environment.
• Familiarity with adversarial machine learning concepts, including adversarial attack techniques (e.g., evasion, poisoning, model inversion, membership inference) and corresponding defensive strategies for protecting AI-powered cybersecurity capabilities.
• Familiarity with the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework and its application to AI threat modeling activities.
• Knowledge of Post-Quantum Cryptography (PQC) principles and their relevance to securing AI model data, weights, and communications within a federal enterprise environment.
• Experience with AI-enhanced threat hunting, including the development of machine learning-driven hunt hypotheses and behavioral analytics models in support of proactive threat hunting activities.
• Familiarity with Zero Trust Architecture principles (NIST SP 800-207, OMB M-22-09) and their application to AI system access control, data governance, and network segmentation within a federal enterprise environment.
• Experience developing AI risk assessments aligned with NIST AI RMF guidelines, including the practical application of the Govern, Map, Measure, and Manage framework functions.
• Familiarity with the secure integration and governance of AI tools such as Perplexity within a federal agency environment, including access control, records management, and privacy compliance considerations.
• Experience with enterprise AI model management platforms for tracking, versioning, and monitoring deployed AI models in production environments.
• Familiarity with Section 508 compliance requirements for AI-generated reports, dashboards, and user-facing AI tools delivered under federal contracts.
• Knowledge of the Factor Analysis of Information Risk (FAIR) methodology as applied to AI risk quantification and enterprise risk management activities.
• Experience contributing to FISMA documentation for AI systems, including system security plan inputs, control implementation descriptions, and continuous monitoring evidence supporting AI system authorizations.

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