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About This Role
Overview:
Thank you for considering IT Concepts dba Kentro, where innovation drives opportunity and collaboration leads to success. Our dynamic community of experts is fully committed to advancing our customers' missions, fostering professional growth, and making a positive impact on our communities.
By joining our supportive community, you will find that Kentro is dedicated to your personal and professional development. Together, we can drive meaningful change, spark innovation, and achieve extraordinary milestones.
Kentro is seeking a Cybersecurity and AI Automation Lead to serve as the senior cybersecurity authority and leads the AI and automation workstream. As senior cyber authority, the Lead advises ATO Managers, ISOs, and the AODR on complex authorization scenarios, validates control inheritance design across area segments, and ensures that processes, training, and reporting accurately reflect federal cybersecurity policy and best practice. As AI \& Automation Lead, the role leads the AI Opportunity Assessment, designs and pilots AI\-enabled process improvements, and scales AI capabilities across the enterprise — identifying standardization opportunities and labor reduction initiatives that deliver measurable enterprise value while preserving security, compliance, and federal AI policy integrity.
Location: This position can be performed remotely within the United States and will support Eastern Time working hours.
Responsibilities:
Senior Cybersecurity Authority
- Provide senior cybersecurity subject matter expertise to the Support Cell, ATO Managers, ISOs, EndPoint Engineering, and the AODR.
- Validate and advise on the inheritance of common controls and the flow of inherited controls into area segments within the CAM Module.
- Review and approve security\-relevant SOPs, transition plan content, training materials, AI pilot designs, and reporting to ensure technical accuracy and policy alignment.
- Provide senior technical authority on RMF activities including categorization, control selection, implementation, assessment, authorization, and continuous monitoring as represented in the CAM Module.
- Support complex or escalated ATO matters and act as a trusted advisor to ATO Managers on risk acceptance and POA\&M decisioning.
- Liaise with the AODR and security leadership on policy interpretation, control implementation strategy, and emerging cybersecurity requirements.
AI \& Automation Leadership
- Lead the AI Opportunity Assessment to identify high\-value AI and automation use cases.
- Design and execute AI pilots, including evaluation criteria, success metrics, and risk controls.
- Identify standardization opportunities where AI and automation can reduce variability and labor across all VA Areas.
- Lead labor reduction initiatives, partnering with the Reporting \& Metrics Lead to quantify and report benefits.
- Ensure AI use cases comply with applicable federal AI policy, NIST AI RMF guidance, security controls, and privacy requirements.
- Partner with the ServiceNow Integration Lead to operationalize AI and automation within the ServiceNow ecosystem (CAM, HAM, SAM, CMDB).
- Provide senior technical expertise on automation architecture, AI tooling, and integration with CAM workflows.
Cross\-Team Integration \& Mentorship
- Mentor junior team members and analysts on technical RMF, CAM, and AI/automation topics.
- Coordinate with the ServiceNow Integration Lead and Reporting \& Metrics Lead to ensure dashboards and reports accurately represent control posture, authorization status, and AI/automation benefits.
- Participate in Support Cell governance forums and represent the team in cross\-stakeholder cybersecurity discussions.
Qualifications:
- Master's degree in Cybersecurity, Information Systems, Computer Science, or related technical field. An additional 10 years of experience may be substituted for the degree requirement for a total of 20\+ years of experience.
- 10\+ years of cybersecurity experience, including at least 5 years in RMF / ATO roles and at least 3 years in automation, AI/ML, or RPA.
- Deep, hands\-on knowledge of NIST SP 800\-37, NIST SP 800\-53, FISMA, and the control inheritance model.
- Working knowledge of federal AI policy, including the NIST AI Risk Management Framework (AI RMF).
- Demonstrated experience advising senior leaders on authorization decisions and risk posture.
- Hands\-on experience implementing AI or automation in regulated environments.
- Working knowledge of ServiceNow GRC, IRM, or CAM modules.
- Strong design and architecture skills.
- CISSP, CISM, or CGRC (formerly CAP) certification.
Preferred Qualifications:* AI/ML credentials (e.g., Microsoft AI certifications, Google Cloud Professional ML Engineer, AWS ML Specialty).
- ServiceNow automation / AIOps experience.
- Prior VA or federal civilian agency RMF experience.
- Experience designing or operating common control provider programs.
- Familiarity with VA\-specific RMF artifacts and processes.
Clearance Requirement:* US Citizen or Green card holder
- Willing and able to obtain and maintain Public Trust Clearance
- Must meet updated ID requirements: https://www.gsa.gov/technology/it\-contract\-vehicles\-and\-purchasing\-programs/federal\-credentialing\-services/get\-appointment\-help/bring\-required\-documents
+ If you do not currently meet the ID requirements outlined, you must be willing and able to update your current forms of ID in a timely manner to complete the suitability process successfully.
Benefits:
The Company
We believe in generating success collaboratively, enabling long\-term mission success, and building trust for the next challenge. With you as our partner, let’s solve challenges, think innovatively, and maximize impact. As a valued member of our team, you have the unique opportunity to work in a diverse range of technology and business career paths, all while supporting our nation and delivering innovative technology solutions. We are a close community of experts that pride ourselves on creating an environment defined by teamwork, dedication, and excellence.
We hold three ISO certifications (27001:2013, 20000\-1:2011, 9001:2015\), two CMMI ML 3 ratings (DEV and SVC) and CMMC Level 2 Certification. Industry Recognition
Growth \| Inc 5000’s Fastest Growing Private Companies, DC Metro List Fastest Growing; Washington Business Journal: Fastest Growing Companies, Top Performing Small Technology Companies in Greater D.C.
Culture \| Northern Virginia Technology Council Tech 100 Honoree; Virginia Best Place to Work; Washington Business Journal: Best Places to Work, Corporate Diversity Index Winner – Mid\-Size Companies, Companies Owned by People of Color; Department of Labor’s HireVets for our work helping veterans transition; SECAF Award of Excellence finalist; Victory Military Friendly Brand; Virginia Values Veterans (V3\); Cystic Fibrosis Foundation Corporate Breath Award Benefits
We offer competitive benefits package including paid time off, healthcare benefits, supplemental benefits, 401k including an employer match, discount perks, rewards, and more. We invest in our employees – Every employee is eligible for education reimbursement for certifications, degrees, or professional development. Reimbursement amounts may fluctuate due to IRS limitations. We want you to grow as an expert and a leader and offer flexibility for you to take a course, complete a certification, or other professional growth and networking. We are committed to supporting your curiosity and sustaining a culture that prioritizes commitment to continuous professional development.
We work hard; we play hard. Kentro is committed to incorporating fun into every day. We dedicate funds for activities – virtual and in\-person – e.g., we host happy hours, holiday events, fitness \& wellness events, and annual celebrations. In alignment with our commitment to our communities, we also host and attend charity galas/events. We believe in appreciating your commitment and building a positive workspace for you to be creative, innovative, and happy. Commitment Equal Opportunity Employment \& VEVRAAKentro is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state or local law.
Kentro is strongly committed to compliance with VEVRAA and other applicable federal, state, and local laws governing equal employment opportunity. We have developed comprehensive policies and procedures to ensure our hiring practices align with these requirements.
As part of our VEVRAA compliance efforts, Kentro has established an equal opportunity plan outlining our commitment to recruiting, hiring, and advancing protected veterans. This plan is regularly reviewed and updated to ensure its effectiveness.
We encourage protected veterans to self\-identify during the application process. This information is strictly confidential and will only be used for reporting and compliance purposes as required by law. Providing this information is voluntary and will not impact your employment eligibility.
Our commitment to equal employment opportunity extends beyond legal compliance. We are dedicated to fostering an inclusive workplace where all employees, including protected veterans, are treated with dignity, respect, and fairness. How to Apply
To apply to Kentro Positions\- Please click on the job link and then click the blue “Apply” button at the top right of Job Description. Please upload your resume and complete all the application steps. You must fully submit the application for Kentro to consider you for a position. If you need alternative application methods, please email [email protected] and request assistance.
Accommodations
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. Reasonable Accommodations may be made to enable qualified individuals with disabilities to perform the essential functions. If you need to discuss reasonable accommodations, please email [email protected].
\#LI\-BK1
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Kentro, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Kentro AI Hiring
Kentro has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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