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About This Role
Koniag IT Systems, LLC, a Koniag Government Services company, is seeking an Agentic AI SME with a Secret Security clearance to support KITS and our government customer. The position is remote.
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 IT Systems, LLC, a Koniag Government Services company, is seeking a highly skilled and innovative ICAM Agentic AI Subject Matter Expert (SME) to join a team supporting Identity, Credential, and Access Management (ICAM) solutions for our government customers. The ideal candidate is a forward\-thinking technology leader with deep expertise in both ICAM platforms and emerging agentic artificial intelligence technologies, passionate about applying cutting\-edge AI capabilities to modernize and automate identity and access management programs in a federal environment. This individual will serve as a primary technical authority on the intersection of agentic AI and ICAM, driving innovation and delivering intelligent, autonomous solutions that enhance security, efficiency, and mission outcomes.
The ICAM Agentic AI SME will serve as the primary technical expert on the application of agentic artificial intelligence technologies within ICAM programs, leveraging platforms such as SailPoint and Okta to design, develop, and implement intelligent, autonomous identity and access management solutions in a federal government environment. This individual will work closely with program managers, engineers, security teams, data scientists, and government stakeholders to identify opportunities for AI\-driven automation, develop agentic AI use cases, and guide the responsible integration of AI capabilities into ICAM workflows and architectures.
Principal responsibilities will include but are not limited to:
- Serve as the primary subject matter expert on agentic AI technologies and their application within ICAM programs, providing technical guidance, recommendations, and thought leadership to program teams and government stakeholders.
- Identify, evaluate, and prioritize opportunities to apply agentic AI capabilities to automate and enhance ICAM processes, including identity lifecycle management, access certifications, anomaly detection, entitlement management, and privileged access governance.
- Design and develop agentic AI solutions that integrate with SailPoint and Okta platforms to enable autonomous decision\-making, intelligent workflow orchestration, and adaptive access control.
- Lead the development of agentic AI use cases, proofs of concept (PoCs), and pilot implementations, translating complex technical concepts into actionable solutions aligned with mission requirements.
- Collaborate with ICAM engineers and administrators to integrate AI\-driven automation into existing SailPoint and Okta environments, leveraging APIs, connectors, and workflow engines.
- Develop and implement AI\-powered anomaly detection and behavioral analytics capabilities to identify suspicious access patterns, insider threats, and potential identity\-related security incidents.
- Guide the responsible and ethical use of AI within ICAM programs, ensuring that agentic AI solutions align with federal AI policies, privacy requirements, and security standards.
- Support the development of AI governance frameworks and policies specific to ICAM, including guidelines for human\-in\-the\-loop oversight, auditability, and explainability of AI\-driven access decisions.
- Provide technical expertise in the evaluation and selection of agentic AI tools, frameworks, and platforms suitable for integration with federal ICAM architectures.
- Stay current with emerging trends and advancements in agentic AI, large language models (LLMs), machine learning, and their applications within identity and access management.
- Develop and deliver technical briefings, white papers, and presentations to government stakeholders and senior leadership on ICAM agentic AI capabilities, roadmaps, and outcomes.
- Collaborate with data engineers and data scientists to develop and maintain the data pipelines, models, and training datasets required to support AI\-driven ICAM capabilities.
- Contribute to the development of technical documentation, architecture diagrams, and standard operating procedures (SOPs) related to agentic AI integrations within ICAM environments.
- Support ATO processes by assessing AI\-related risks and contributing to security documentation, including System Security Plans (SSPs) and Risk Assessment Reports (RARs).
- Mentor and provide technical guidance to ICAM engineers and administrators on AI concepts, tools, and best practices.
Education and Experience:Required:
- Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Cybersecurity, or a related field from an accredited college or university; equivalent work experience may be considered in lieu of a degree.
- 5\+ years of experience in identity and access management, with hands\-on expertise in SailPoint and/or Okta platforms.
- 3\+ years of experience working with artificial intelligence, machine learning, or automation technologies, with demonstrated experience applying AI capabilities to enterprise IT or cybersecurity use cases.
- Experience working in a federal government IT environment.
Clearance Requirement:
- Active Secret Clearance required.
Required Skills and Competencies:
- Exceptional communication skills in English – both written and oral – with the ability to translate complex AI and ICAM concepts for both technical and non\-technical audiences, including senior government leadership.
- Deep hands\-on expertise with SailPoint IdentityIQ (IIQ) or SailPoint Identity Security Cloud (ISC), including identity lifecycle management, access certifications, role management, and provisioning workflows.
- Strong hands\-on experience with the Okta platform, including SSO, MFA, Universal Directory, Lifecycle Management, Okta Workflows, and API Access Management.
- Demonstrated knowledge of agentic AI concepts, frameworks, and technologies, including autonomous agents, multi\-agent systems, large language models (LLMs), and AI orchestration platforms.
- Experience designing and implementing AI\-driven automation solutions, including the use of AI agents to perform autonomous task execution, decision\-making, and workflow orchestration within enterprise environments.
- Strong understanding of machine learning concepts and the ability to apply them to ICAM use cases such as anomaly detection, behavioral analytics, and intelligent access recommendations.
- Familiarity with AI frameworks and tools such as LangChain, AutoGen, OpenAI API, or similar agentic AI platforms.
- Experience integrating AI capabilities with enterprise platforms via REST APIs, SCIM, and webhooks.
- Strong understanding of ICAM frameworks and federal identity management standards, including FICAM, NIST SP 800\-63, and Zero Trust Architecture principles.
- Knowledge of federal AI policies and responsible AI frameworks, including OMB guidance on AI governance and NIST AI Risk Management Framework (AI RMF).
- Ability to assess and articulate AI\-related risks within ICAM environments and recommend appropriate mitigations.
- Experience developing technical documentation, architecture diagrams, white papers, and briefings for diverse audiences.
- Demonstrated ability to lead technical initiatives and provide subject matter expertise in a team\-oriented, fast\-paced environment.
- Active Secret Clearance.
Desired Skills and Competencies:
- Experience working on large\-scale federal ICAM modernization programs or Zero Trust implementation initiatives.
- Relevant SailPoint certifications (e.g., SailPoint Certified IdentityIQ Engineer, SailPoint Identity Security Cloud Engineer).
- Relevant Okta certifications (e.g., Okta Certified Administrator, Okta Certified Consultant, Okta Certified Developer).
- Experience with AI/ML platforms such as AWS SageMaker, Azure AI, or Google Vertex AI and their integration with identity management solutions.
- Familiarity with Privileged Access Management (PAM) platforms such as CyberArk or BeyondTrust and opportunities for AI\-driven automation within PAM workflows.
- Experience with natural language processing (NLP) and conversational AI interfaces applied to ICAM self\-service and helpdesk automation use cases.
- Knowledge of graph\-based identity analytics and the use of knowledge graphs for entitlement visualization and risk scoring.
- Understanding of PIV/CAC authentication and its role within federal ICAM architectures.
- Experience supporting ATO processes and contributing to security documentation such as SSPs, RARs, and POA\&Ms.
- Familiarity with DevSecOps practices and CI/CD pipeline integrations involving AI and identity services.
- CISSP, CISM, or other relevant cybersecurity certifications.
- Experience with Python, PowerShell, or other scripting languages for AI model integration and ICAM automation tasks.
- Familiarity with explainable AI (XAI) concepts and their application to auditable, transparent access control decision\-making in federal environments.
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
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.
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.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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