AI/ML Engineer IV

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-deploy…