Note: The job is a remote job and is open to candidates in USA. Kentro is hiring an AI/ML Engineer IV to lead artificial intelligence and machine learning engineering for the Federal Aviation Administration’s ATLAS modernization program. The role defines AI strategy and shared platform architecture, establishes MLOps and responsible AI practices, leads AI-assisted legacy modernization, and delivers production AI capabilities while providing technical leadership across delivery teams.
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
Skills
- 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
- US Citizen
- 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
Benefits
- Remote work within the United States, supporting Eastern Time working hours
- Paid time off
- Healthcare benefits
- Supplemental benefits
- 401k including an employer match
- Discount perks
- Rewards
- Every employee is eligible for education reimbursement for certifications, degrees, or professional development; reimbursement amounts may fluctuate due to IRS limitations
- Flexibility to take a course, complete a certification, or pursue other professional growth and networking
- Company-funded virtual and in-person activities, including happy hours, holiday events, fitness and wellness events, and annual celebrations
- Opportunities to host and attend charity galas and events
Company Overview