AI/ML Engineer IV at Kentro

Job description

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 anticipated base salary range for this position is $165,000–$185,000 annually and reflects Kentro’s good-faith expectations at the time of posting. Final compensation is determined based on the role’s requirements, relevant skills, experience, education, certifications, work location, applicable security clearance requirements, market conditions, internal equity, and contract requirements. 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. *This position is contingent upon contract award* 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 & VEVRAA Kentro 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 careers@kentro.us 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 careers@kentro.us . #LI-SB1

Requirements

Must-haves

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.
Location: Remote - This position can be performed remotely within the United States and will support Eastern Time working hours.
This position is contingent upon contract award*
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
Design and deliver production AI capabilities for modernized systems, including retrieval-augmented generation, document and text processing, classification, forecasting, and anomaly detection
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
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
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)

Nice-to-haves

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
Existing FAA suitability determination or transferable federal background investigation strongly preferred

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlci/cdmachine learningpythonawsazuregcp

Platform: jibe
Effort: medium

Benefits

Benefits detected in the listing.

401k

Paid Time Off

Wellness

Professional Development