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About This Role
Koniag Data Solutions, LLC, a Koniag Government Services company, is seeking an AI Security Specialist/SME to support KDS and our government customer in Washington, DC. This position requires the candidate to be able to obtain a Public Trust.
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.
Koniag Data Solutions, a Koniag Government Services company, is seeking an experienced AI Security Specialist / Subject Matter Expert (SME) to support the development, implementation, and management of artificial intelligence security programs, policies, and risk management initiatives in support of the U.S. Small Business Administration (SBA). The ideal candidate is a senior\-level cybersecurity and AI professional with deep expertise in AI/ML security principles, AI risk management frameworks, and the unique security challenges and considerations associated with the adoption, development, and operation of AI systems within a federal government environment. This individual will serve as SBA's subject matter expert on AI security matters, providing expert guidance and technical leadership to ensure AI systems and initiatives across the SBA are developed, deployed, and operated securely, responsibly, and in compliance with applicable federal AI policies, executive orders, and emerging regulatory requirements.
The AI Security Specialist / SME will serve as the senior technical lead and subject matter expert for AI security at the SBA, working closely with SBA's IT leadership, security teams, data science and AI development teams, program managers, legal counsel, and external partners to develop and implement a comprehensive AI security program that enables SBA to adopt and leverage AI technologies securely, responsibly, and in alignment with federal AI governance requirements.
Principal responsibilities will include but are not limited to:
- Serve as SBA's senior SME and technical lead for AI security, providing expert guidance, strategic direction, and technical leadership on all matters related to the security of AI systems, machine learning models, and AI\-enabled applications across the SBA enterprise.
- Develop, implement, and maintain SBA's AI security program, including the development of AI security policies, standards, guidelines, and risk management frameworks aligned with applicable federal AI executive orders, OMB AI guidance, NIST AI Risk Management Framework (AI RMF), and other relevant federal AI governance requirements.
- Lead the conduct of AI security risk assessments and reviews for SBA AI systems and initiatives, evaluating AI\-specific security risks including adversarial attacks, model poisoning, data integrity threats, model inversion, and other AI/ML\-specific threat vectors, and developing comprehensive risk mitigation strategies.
- Provide expert security guidance and oversight throughout the AI system lifecycle, from requirements and design through development, testing, deployment, and operations, ensuring AI security requirements are properly integrated at each phase.
- Develop and maintain AI security assessment methodologies, testing frameworks, and evaluation criteria for assessing the security and trustworthiness of AI systems and machine learning models deployed within the SBA environment.
- Collaborate with SBA's data science, AI development, and application teams to integrate AI security best practices, secure development guidelines, and security testing requirements into AI development workflows and processes.
- Lead the development and implementation of AI governance documentation for SBA, including AI use case inventories, AI impact assessments, AI system security plans, and other required AI governance artifacts in alignment with federal AI transparency and accountability requirements.
- Monitor and analyze the AI threat landscape, including emerging adversarial AI attack techniques, AI vulnerability disclosures, and evolving federal AI security guidance, providing timely threat assessments and recommendations to SBA security leadership.
- Provide expert guidance on the security implications of generative AI technologies and large language models (LLMs), including the development of SBA policies and guidelines for the secure and responsible use of generative AI tools by SBA personnel and contractors.
- Collaborate with SBA's privacy team to address the intersection of AI security and privacy, including AI\-related privacy risks, data minimization requirements, and the privacy implications of AI model training, deployment, and output
- Support the integration of AI security considerations into SBA's existing cybersecurity frameworks, risk management processes, and security authorization activities, including the development of AI\-specific security control overlays and assessment procedures.
- Serve as SBA's representative and technical SME in engagements with OMB, CISA, NIST, and other federal partners on AI security\-related matters, including participation in interagency AI security working groups and forums.
- Develop and deliver comprehensive AI security awareness and training programs for SBA personnel and contractors, ensuring all staff understand the security risks, responsibilities, and requirements associated with the use and development of AI systems.
- Lead and support after\-action reviews and lessons learned following AI security incidents or near\-misses, translating findings into actionable improvements to SBA's AI security program and practices
- Develop and deliver AI security briefings, program reports, and strategic recommendations for SBA senior leadership and oversight bodies, providing clear and compelling insights into AI security risks, program status, and compliance posture.
- Stay at the forefront of AI security research, technology developments, federal policy updates, and industry best practices, applying this knowledge to continuously refine and strengthen SBA's AI security program.
Education and Experience:Required:
- Bachelor's degree in Cybersecurity, Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field from an accredited college or university, OR equivalent combination of education and extensive relevant work experience.
- 7\+ years of progressive experience in cybersecurity, with significant demonstrated experience in AI/ML security, data science security, or a closely related field within a federal government or large enterprise environment.
- Demonstrated expert\-level knowledge of AI/ML security principles, adversarial machine learning concepts, and AI\-specific threat vectors including model poisoning, adversarial attacks, model inversion, and data integrity threats
- Demonstrated familiarity with federal AI governance frameworks and requirements including NIST AI Risk Management Framework (AI RMF), Executive Order 13960, Executive Order 14110, and applicable OMB AI memoranda and guidance.
- One or more of the following certifications: Certified Information Systems Security Professional (CISSP), Certified Information Security Manager (CISM), or equivalent senior\-level cybersecurity certification, supplemented by demonstrated AI/ML security expertise.
Desired:
- Master's degree or Ph.D. in Cybersecurity, Computer Science, Artificial Intelligence, Data Science, or a related field.
- Prior experience serving as an AI security SME or advisor supporting a federal civilian agency, preferably the SBA or a similar organization.
- Certified Artificial Intelligence Security Specialist (CAISS) or equivalent AI security certification, or active pursuit of same.
- Demonstrated experience with AI red teaming, adversarial robustness testing, or AI penetration testing methodologies.
Required Skills and Competencies:
- Expert\-level knowledge of AI and machine learning concepts, architectures, and development methodologies, with the ability to apply this knowledge to identify, assess, and mitigate security risks across a broad range of AI system types and use cases.
- Deep expertise in AI/ML security principles and adversarial machine learning, including comprehensive understanding of AI\-specific threat vectors such as adversarial examples, model poisoning, backdoor attacks, model inversion, membership inference, and data poisoning attacks.
- Demonstrated ability to develop and implement comprehensive AI security risk assessment methodologies, testing frameworks, and evaluation criteria for assessing the security and trustworthiness of federal AI systems.
- Expert\-level knowledge of federal AI governance frameworks and requirements including NIST AI RMF, applicable executive orders, and OMB AI guidance, with the ability to translate these requirements into practical AI security program activities and controls.
- Strong experience collaborating with data science and AI development teams to integrate security best practices and requirements into AI development workflows, MLOps pipelines, and AI system architectures.
- Solid understanding of the security implications of generative AI technologies and large language models (LLMs), including prompt injection attacks, data leakage risks, model abuse scenarios, and strategies for the secure and responsible deployment of generative AI capabilities.
- Experience developing AI governance documentation including AI use case inventories, AI impact assessments, and AI system security documentation in alignment with federal AI transparency and accountability requirements.
- Strong understanding of the intersection of AI security with privacy, data governance, and ethics, including the ability to address AI\-related privacy risks and data protection requirements within an AI security program context.
- Solid understanding of federal cybersecurity frameworks and requirements including NIST SP 800\-53, FISMA, and relevant OMB and CISA guidance, and their relationship to AI security program development and implementation.
- Exceptional written and oral communication skills in English, with the ability to clearly communicate complex AI security concepts, risk assessments, and strategic recommendations to both technical teams and senior non\-technical audiences including agency leadership.
- Strong leadership skills with demonstrated experience providing expert technical guidance and oversight on AI security matters to cross\-functional teams including security, IT, data science, legal, and program management staff.
- Ability to obtain and maintain a Public Trust clearance as required by the SBA.
Desired Skills and Competencies:
- Prior experience serving as an AI security SME or advisor supporting the SBA or a similar federal civilian agency.
- In\-depth familiarity with SBA's AI initiatives, IT environment, and mission areas, including an understanding of the unique AI security challenges and opportunities associated with SBA's specific use cases and operational context
- Demonstrated experience with AI red teaming, adversarial robustness testing, or AI penetration testing methodologies, including hands\-on experience using adversarial machine learning tools and frameworks such as IBM Adversarial Robustness Toolbox (ART), Microsoft Counterfit, or similar.
- Advanced knowledge of MLOps practices and platforms and their security implications, including experience integrating AI security controls and testing into ML pipelines and model deployment workflows.
- Experience with AI supply chain security considerations, including the security risks associated with pre\-trained models, open\-source AI libraries, and third\-party AI services and APIs.
- Familiarity with AI fairness, explainability, and transparency concepts and their relationship to AI security and trustworthiness in a federal government context.
- Experience with cloud\-based AI/ML platforms and services such as AWS SageMaker, Microsoft Azure Machine Learning, or Google Vertex AI, and their security configurations, access controls, and monitoring capabilities.
- Knowledge of natural language processing (NLP) and computer vision AI application security considerations and specific threat vectors relevant to these AI domains.
- Experience participating in federal interagency AI security working groups, NIST AI RMF development activities, or OMB AI governance oversight engagements.
- Certified Artificial Intelligence Security Specialist (CAISS), AI Security certification from a recognized professional organization, or other relevant advanced AI or cybersecurity certifications
- Experience developing and delivering AI security training programs for large, diverse federal agency workforces including both technical and non\-technical personnel.
- Familiarity with international AI governance frameworks and standards such as the EU AI Act, ISO/IEC 42001, and their relationship to federal AI security and governance requirements.
Our 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 employment.
The company is dedicated to seeking all qualified applicants. If you require an accommodation to navigate or apply for a position on our website, please get in touch with Heaven Wood via e\-mail at accommodations@koniag\-gs.com or by calling 703\-488\-9377 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 www.koniag\-gs.com.
Equal Opportunity Employer/Veterans/Disabled. Shareholder Preference in accordance with Public Law 88\-352
Salary Context
This $190K-$212K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Koniag Government Services, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($201K) sits 8% below the category median. Disclosed range: $190K to $212K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Koniag Government Services AI Hiring
Koniag Government Services has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, Remote, US. Compensation range: $134K - $212K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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