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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 hiring an AI/ML Engineer IV to serve as lead AI/ML engineer on the Federal Aviation Administration’s (FAA) Accelerated Transformation of Legacy Applications and Systems (ATLAS) program — one of the largest civilian IT modernization efforts in the federal government. ATLAS covers a mission-support portfolio of more than 200 applications and roughly 3,000 databases, and artificial intelligence is named directly in the modernization objectives: intelligent automation, enterprise data excellence, and AI integrated into how the portfolio is both transformed and operated.
In this role you define the AI engineering strategy and shared AI platform architecture for the program in alignment with enterprise architecture, data governance, cybersecurity, and program priorities. You bring mastery of AI/ML technologies, MLOps, and responsible AI practice, and you apply it to two distinct problems: using AI to accelerate the modernization itself — code understanding, automated transformation, test generation, documentation recovery from undocumented legacy systems — and building the AI capabilities that modernized FAA systems will run on.
You lead AI initiatives across the portfolio, establish shared AI engineering capabilities that other teams build on, drive innovation from pilot through production, and provide technical leadership for AI transformation. In a federal aviation context that means governance is not an afterthought: every model you put into production has to be explainable, monitored, documented, and defensible under federal AI policy. Location: Remote - This position can be performed remotely within the United States and will support Eastern Time working hours. Compensation Range: The pay range for this position is $165,000-196,000 annually. Kentro determines compensation by evaluating current government contracting and commercial market conditions, as well as the role’s specific requirements. Final salary placement within this range will be based on the candidate’s relevant skills, experience, education, certifications, location, and security clearance level, where applicable.
This Job Description reflects the primary duties of the role; however, it is not intended to be all‑inclusive. Team members may be asked to take on additional responsibilities in alignment with customer expectations, business needs, and Kentro’s culture of collaboration and adaptability. Responsibilities:
- Define and lead the AI/ML engineering strategy for the portfolio in alignment with enterprise architecture, data governance, cybersecurity, and program priorities; determine where AI creates measurable value and sequence capabilities accordingly
- Architect the shared AI engineering platform — approved model and provider access, prompt and agent orchestration, retrieval-augmented generation, evaluation, model registry and serving, monitoring, and traditional training infrastructure where justified — as shared capability rather than per-project tooling
- Establish end-to-end MLOps practice: automated training and evaluation pipelines, CI/CD for models, versioning of data, code and models, reproducibility, and controlled promotion to production
- Lead AI-assisted modernization initiatives: apply LLMs and code-transformation tooling to legacy code comprehension, documentation recovery, automated refactoring, test generation, and data model inference across a large enterprise including legacy systems
- Design and deliver production AI capabilities for modernized systems, including retrieval-augmented generation, document and text processing, classification, forecasting, and anomaly detection
- Implement the technical controls that support the program’s responsible AI practice, in partnership with architecture, cybersecurity, data governance, and program leadership: bias and fairness evaluation, explainability, human-in-the-loop design, model cards and documentation, red-teaming, and pre-deployment risk assessment
- Engineer AI systems to satisfy applicable federal obligations, including OMB AI policy for federal agency use (currently M-25-21), the NIST AI Risk Management Framework, FISMA and NIST SP 800-53 controls, and applicable FAA and DOT guidance
- Define model evaluation standards and the monitoring regime for production models: drift detection, performance degradation, data quality gates, and retraining triggers
- Define AI workload requirements for the data engineering interface — pipelines, feature stores, labeling strategy, provenance, and data quality — in partnership with the enterprise data architecture lead
- Recommend which class of AI fits each use case — classical ML, generative AI, or agentic systems — and be responsible for assessing the implications that follow from that choice, including the security boundary and data exposure, privacy and data residency, inference and token cost at scale, evaluation difficulty, and long-term sustainment burden
- Lead technical evaluation and recommendations for build-versus-buy and model selection decisions across commercial, open-weight, and cloud-provider models, accounting for cost, latency, data residency, and federal security constraints
- Drive innovation: identify emerging AI capability, run structured pilots with defined success criteria, and productize what proves out into standard practice
- Provide technical leadership, design review, and code review to AI/ML engineers and data scientists across delivery teams; mentor engineers new to production ML
- Advise program leadership and FAA stakeholders on AI feasibility, risk, and realistic expectations
Qualifications:
- Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related technical discipline
- 8–10 years of progressive software engineering and AI/ML experience, including demonstrated technical leadership on production ML systems
- Proven record delivering machine learning systems into production and operating them over time — not research or proof-of-concept work alone
- Expert proficiency in Python and the modern ML stack (PyTorch or TensorFlow, scikit-learn, pandas, and related tooling)
- Deep MLOps/LLMOps experience: training and deployment pipelines, model and prompt versioning, experiment tracking, automated evaluation, and production monitoring
- Hands-on experience with large language models in production, including prompt engineering, retrieval-augmented generation, fine-tuning or adaptation, evaluation, and cost and latency management
- Cloud ML platform expertise (AWS SageMaker/Bedrock, Azure ML/OpenAI Service, or GCP Vertex AI), including containerized and scalable serving architectures
- Strong data engineering foundation: pipelines, feature engineering at scale, and working with imperfect data from legacy sources
- Demonstrated judgment in selecting among classical ML, generative AI, and agentic approaches, with the ability to justify the choice on security, privacy, cost, and sustainment grounds
- Demonstrated application of responsible AI practice — fairness, explainability, documentation, and risk assessment — in a regulated or high-consequence setting
- Solid software engineering fundamentals: version control, testing, code review, and CI/CD discipline applied to ML code
- Ability to set standards other teams adopt, and to communicate AI capability and limitation credibly to non-technical executive and government audiences
Preferred Qualifications:* Experience supporting FAA, DOT, or comparable federal civilian programs, particularly where AI governance and ATO processes applied
- Direct experience applying AI to legacy modernization — code comprehension, automated transformation, or documentation recovery at portfolio scale
- Familiarity with federal AI governance in practice: OMB AI policy compliance, agency AI use case inventories, and high-impact AI determinations
- Experience with the NIST AI Risk Management Framework applied to a real deployment rather than in the abstract
- Background in aviation, transportation, or another safety-critical domain
- Experience standing up an AI/ML engineering platform or AI center of excellence from scratch
- Relevant certifications: AWS Machine Learning Specialty, Azure AI Engineer, or GCP Professional ML Engineer
- Experience with agentic systems, tool use, and orchestration frameworks in production
- Publications, open-source contributions, or conference presentations in applied ML
Clearance Requirement:* US Citizen
- Existing FAA suitability determination or transferable federal background investigation strongly preferred
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-
Salary Context
This $165K-$196K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($180K) sits 15% below the category median. Disclosed range: $165K to $196K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Kentro AI Hiring
Kentro has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $196K - $196K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
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