AI Security Engineer Mid

Koniag Data Solutions a Koniag Government Services company is seeking an experiencedAI 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 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 clients enterprise cybersecurity ecosystemincluding AI-powered threat detection automated compliance monitoring machine learning-driven risk assessment and AI-enhanced Security Information and Event Management (SIEM) capabilitiesin alignment with applicable federal AI governance frameworks NIST guidelines and agency cybersecurity 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 AI Security Engineer (Mid) will serve as a key technical contributor within the programs 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 clients enterprise environment. This individual is responsible for supporting the full lifecycle of AI security engineering activitiesfrom requirements analysis and solution design through development integration testing deployment and ongoing optimizationensuring all AI capabilities are secure governed compliant and effectively integrated into operational cybersecurity responsibilities will include but are not limited to:AI Security Engineering & ImplementationSupport 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 clients enterprise cybersecurity 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 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 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 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 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 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 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 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 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 Model Development & OptimizationDevelop 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 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 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 and implement algorithmic optimizations to improve the computational efficiency resource utilization and detection accuracy of deployed AI and machine learning models within the enterprise 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 all model development activities including data sources preprocessing steps model architectures training parameters evaluation metrics and deployment configurations maintaining comprehensive model documentation in the programs designated knowledge management Governance & Compliance SupportSupport the implementation and maintenance of the programs 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 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 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 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 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 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 Collaboration & DocumentationCollaborate 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 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 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 programs designated 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 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 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 and Experience:Required:Bachelors degree in computer science Artificial Intelligence Cybersecurity Information Systems Data Science or a related field from an accredited college or 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 experience developing training and deploying machine learning models for security or data analytics applications including anomaly detection behavioral analytics predictive modeling or classification use integrating AI or machine learning capabilities into enterprise security tools or operational with federal cybersecurity frameworks and standards including FISMA NIST SP 800-53 and applicable AI governance 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 :Masters degree in Artificial Intelligence Machine Learning Cybersecurity Computer Science Data Science or a related experience supporting federal civilian agency AI security or cybersecurity programs in an engineering or technical contributor working on GSA Multiple Award Schedule (MAS) HACS SIN contracts or comparable federal IT cybersecurity contract with the NIST AI Risk Management Framework (AI RMF) and its practical application within a federal agency cybersecurity Skills and Competencies:Strong communication skills in Englishboth written and oralwith the demonstrated ability to clearly explain AI security engineering concepts model development findings and technical recommendations to both technical peers and non-technical Government 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 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 with scripting and automation languages including Python PowerShell SQL and JSON for AI model development data pipeline construction feature engineering and cybersecurity workflow 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 developing and maintaining CI/CD pipelines for automated AI model updates and performance monitoring within a DevSecOps delivery 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 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 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 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 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 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 assessing supply chain risks associated with third-party AI tools models and data sources within a federal IT Skills and Competencies:CompTIA Security Certified Ethical Hacker (CEH) Certified Information Systems Security Professional (CISSP) or equivalent cybersecurity 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 with AWS AI and machine learning security services (e.g. Amazon GuardDuty ML Amazon Macie AWS Security Hub) within a FedRAMP-compliant cloud 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 with the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework and its application to AI threat modeling of Post-Quantum Cryptography (PQC) principles and their relevance to securing AI model data weights and communications within a federal enterprise with AI-enhanced threat hunting including the development of machine learning-driven hunt hypotheses and behavioral analytics models in support of proactive threat hunting 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 developing AI risk assessments aligned with NIST AI RMF guidelines including the practical application of the Govern Map Measure and Manage framework 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 with enterprise AI model management platforms for tracking versioning and monitoring deployed AI models in production with Section 508 compliance requirements for AI-generated reports dashboards and user-facing AI tools delivered under federal of the Factor Analysis of Information Risk (FAIR) methodology as applied to AI risk quantification and enterprise risk management contributing to FISMA documentation for AI systems including system security plan inputs control implementation descriptions and continuous monitoring evidence supporting AI system Equal Employment Opportunity Policy:The company is an equal opportunity employer. The company shall not discriminate against any employee or applicant because of race color religion creed ethnicity sex sexual orientation gender or gender identity (except where gender is a bona fide occupational qualification) national origin or ancestry age disability citizenship military/veteran status marital status genetic information or any other characteristic protected by applicable federal state or local law. We are committed to equal employment opportunity in all decisions related to employment promotion wages benefits and all other privileges terms and conditions of company is dedicated to seeking all qualified applicants. If you require an accommodation to navigate or to apply to a position on our website please contact Heaven Wood via e-mail at or by calling to request accommodations. Koniag Government Services (KGS) is an Alaska Native Owned corporation supporting the values and traditions of our native communities through an agile employee and corporate culture that delivers Enterprise Solutions Professional Services and Operational Management to Federal Government Agencies. As a wholly owned subsidiary of Koniag we apply our proven commercial solutions to a deep knowledge of Defense and Civilian missions to provide forward leaning technical professional and operational solutions. KGS enables successful mission outcomes for our customers through solution-oriented business partnerships and a commitment to exceptional service delivery. We ensure long-term success with a continuous improvement approach while balancing the collective interests of our customers employees and native communities. For more information please visit Opportunity Employer/Veterans/Disabled. Shareholder Preference in accordance with Public Law 88-352