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About This Role
Koniag Data Solutions, LLC, a Koniag Government Services company, is seeking an AI Governance Analyst 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 Government, a Koniag Government Services company, is seeking an experienced AI Governance Analyst to support the U.S. Small Business Administration (SBA). The ideal candidate is a knowledgeable and forward\-thinking professional with expertise in artificial intelligence governance, policy development, and risk management within federal government environments. This individual will play a critical role in supporting SBA's responsible adoption and use of artificial intelligence technologies, ensuring that AI systems and initiatives align with federal AI policies, ethical principles, and applicable laws and regulations.
The AI Governance Analyst will serve as a key contributor to SBA's AI governance program, supporting the development, implementation, and management of AI governance frameworks, policies, and processes that ensure the responsible, ethical, and compliant use of AI technologies across the agency.
Principal responsibilities will include but are not limited to:
- Support the development, implementation, and maintenance of SBA's AI governance framework, policies, standards, and procedures, ensuring alignment with federal AI policies, executive orders, OMB guidance, and applicable laws and regulations governing the use of AI in federal agencies.
- Conduct AI risk assessments and impact analyses for AI systems and initiatives across SBA, identifying potential risks related to bias, fairness, transparency, explainability, privacy, security, and civil liberties, and developing mitigation strategies and recommendations.
- Support SBA's compliance with applicable federal AI requirements, including Executive Order 13960 (Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government), Executive Order 14110 (Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence), OMB Memorandum M\-24\-10, and other relevant federal AI directives and guidance.
- Assist in the development and maintenance of SBA's AI inventory, ensuring all AI systems and use cases are accurately identified, documented, and reported in compliance with federal AI inventory requirements established by OMB guidance.
- Collaborate with SBA program offices, IT teams, data scientists, legal counsel, and privacy officers to embed AI governance requirements into the design, procurement, development, and deployment of AI systems and solutions throughout the AI lifecycle.
- Develop and maintain AI governance documentation, including AI use case assessments, AI impact assessments, AI risk registers, governance checklists, and AI system documentation aligned with NIST AI Risk Management Framework (AI RMF) requirements.
- Support the application of the NIST AI Risk Management Framework (NIST AI RMF 1\.0\) to SBA's AI initiatives, assisting in the GOVERN, MAP, MEASURE, and MANAGE functions to identify, assess, and mitigate AI\-related risks across the agency.
- Monitor and analyze developments in federal AI policy, legislation, executive orders, OMB guidance, and industry best practices, assessing their impact on SBA's AI governance program and supporting the implementation of required program updates.
- Develop and deliver AI governance awareness and training materials for SBA personnel, contractors, and leadership, promoting a strong understanding of responsible AI principles, federal AI requirements, and SBA's AI governance policies and procedures.
- Support the integration of AI governance and security requirements into SBA's NIST Risk Management Framework (RMF) and Authorization to Operate (ATO) processes, ensuring AI\-specific risks and controls are appropriately documented and assessed.
- Collaborate with SBA's privacy, cybersecurity, and legal teams to address the intersection of AI governance with privacy, data protection, cybersecurity, and civil rights and civil liberties considerations.
- Conduct vendor and procurement assessments for AI tools and solutions, evaluating proposed AI systems against SBA's AI governance requirements, federal standards, and ethical AI principles.
- Support the development and implementation of AI monitoring and auditing processes to ensure deployed AI systems continue to perform as intended, remain free from significant bias or drift, and comply with applicable governance requirements over their operational lifecycle.
- Prepare and deliver reports, briefings, and presentations on AI governance program status, AI risk findings, and policy compliance updates to SBA leadership and stakeholders.
- Participate in interagency AI governance working groups, communities of practice, and knowledge sharing forums, representing SBA's interests and staying current with emerging federal AI governance trends and requirements.
Education and Experience:Required:
- Bachelor's degree in Information Technology, Computer Science, Public Policy, Law, Data Science, Cybersecurity, or a related field from an accredited college or university.
- 5\+ years of progressive experience in AI governance, technology policy, risk management, information assurance, or a closely related field, with demonstrated experience supporting AI governance or responsible AI initiatives.
- Demonstrated knowledge of federal AI policies, executive orders, and guidance, including OMB Memorandum M\-24\-10, Executive Order 14110, and NIST AI RMF 1\.0\.
- Experience working within or supporting federal government agencies on technology governance, risk management, or compliance programs.
- One or more of the following certifications or equivalent:
- Certified Information Systems Security Professional (CISSP)
- Certified Information Privacy Professional/Government (CIPP/G)
- NIST AI RMF credentials or equivalent AI governance training and certification
- Project Management Professional (PMP) or equivalent
- Certified in Risk and Information Systems Control (CRISC)
Desired:
- Master's degree in Artificial Intelligence, Data Science, Public Policy, Law, Cybersecurity, Information Management, or a related field.
- 7\+ years of experience in AI governance, technology policy, or risk management, with a strong background supporting federal civilian agency AI programs.
- Experience with AI/ML model development, data science methodologies, or AI system engineering, providing technical grounding for governance and risk assessment activities.
Required Skills and Competencies:
- Exceptional communication skills in English – both written and oral – with the ability to clearly explain complex AI governance concepts, risk findings, and policy requirements to diverse audiences, including technical staff, program managers, legal counsel, and senior agency leadership.
- Strong knowledge of federal AI governance requirements and frameworks, including Executive Order 14110, OMB Memorandum M\-24\-10, the NIST AI Risk Management Framework (AI RMF 1\.0\), and NIST AI standards and guidelines.
- Demonstrated understanding of AI and machine learning concepts, including supervised and unsupervised learning, natural language processing, computer vision, large language models (LLMs), and generative AI, sufficient to effectively assess AI governance and risk considerations.
- Experience conducting AI risk assessments and impact analyses, identifying risks related to algorithmic bias, fairness, transparency, explainability, privacy, security, and civil liberties, and developing practical risk mitigation strategies
- Knowledge of responsible AI principles, including fairness, accountability, transparency, explainability, safety, and reliability, and their application within federal agency AI governance programs.
- Familiarity with federal privacy laws and regulations, including the Privacy Act of 1974, OMB Circular A\-130, and NIST SP 800\-122, and their intersection with AI data collection, processing, and use
- Experience developing and maintaining governance documentation, including policy documents, risk registers, assessment reports, and compliance checklists.
- Understanding of the federal procurement and acquisition process and experience evaluating AI tools and solutions against governance, security, and ethical AI requirements
- Knowledge of cybersecurity frameworks and their relationship to AI system security, including NIST SP 800\-53 controls applicable to AI systems and the integration of AI governance into the RMF and ATO processes
- Strong analytical and critical thinking skills, with the ability to assess complex AI governance challenges, interpret evolving federal policy requirements, and develop practical, actionable recommendations.
- Ability to collaborate effectively with cross\-functional teams, including IT, data science, legal, privacy, and program office stakeholders, to integrate AI governance requirements into agency operations.
- Strong organizational and program management skills, with the ability to manage multiple concurrent priorities and deliver high\-quality work products under tight deadlines.
- Ability to obtain and maintain a Public Trust Clearance.
Desired Skills and Competencies:
- Prior experience supporting SBA or other federal civilian agency AI governance or responsible AI programs.
- Hands\-on experience with AI/ML model development, training, and evaluation, including familiarity with common AI/ML frameworks and platforms such as TensorFlow, PyTorch, Scikit\-learn, or equivalent.
- Experience with AI bias detection and mitigation tools and methodologies, including fairness metrics, bias auditing frameworks, and algorithmic impact assessment techniques.
- Familiarity with generative AI and large language model (LLM) governance considerations, including prompt engineering risks, hallucination, data poisoning, model inversion, and related security and trustworthiness challenges.
- Knowledge of AI explainability and interpretability techniques, including SHAP, LIME, and other model explanation methodologies, and their application in federal AI governance and accountability requirements.
- Experience with AI system monitoring and auditing tools and techniques, including model performance monitoring, drift detection, and automated compliance checking.
- Familiarity with international AI governance frameworks and standards, including the EU AI Act, ISO/IEC 42001, and IEEE standards for AI ethics and governance, and their potential implications for federal agency AI programs.
- Experience supporting federal agency responses to congressional inquiries, inspector general (IG) reviews, or GAO assessments related to AI or emerging technology governance.
- Knowledge of data governance principles and their intersection with AI governance, including data quality management, data lineage, and metadata management as they relate to AI system inputs and outputs.
- Familiarity with cloud\-based AI and machine learning platforms, including AWS SageMaker, Azure Machine Learning, and Google Vertex AI, and the governance considerations associated with cloud\-hosted AI solutions.
- Experience with algorithmic auditing, third\-party AI system assessments, and the development of AI procurement evaluation criteria and contractual AI governance requirements.
- Certified Ethical Emerging Technologist (CEET) or equivalent AI ethics and governance certification.
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 $110K-$134K range is in the lower quartile 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 ($122K) sits 44% below the category median. Disclosed range: $110K to $134K.
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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