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# AI/ML Engineer IV

- **Company:** Kentro LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $165,000.0 - $196,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Code Review, Cyber Security, Databases, Continuous Integration, Information Engineering, Data Governance, Text Processing, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Software Engineering, Management of Software Versions, Google Cloud, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Generative AI, Pandas, AI Platforms, Scikit Learn, Kubernetes, Information Technology, Free and Open-Source Software, Machine Learning Operations, Software Version Control, Legacy Systems
- **Published:** September 29, 2026
- **Apply:** https://www.dice.com/job-detail/a32b7d9e-6c9d-4a0f-b09e-d4c755fb82da

## About the Role

* 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 Google Cloud Platform 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, * 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 Google Cloud Platform 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

## Description

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., * 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


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