AI/ML Architect, SME

General Dynamics Information Technology

. Define, document, and maintain a scalable, modular AI/ML architecture aligned with enterprise cloud strategy and CMM Program requirements .

Posted 10/13/2026full-timeRemote • United StatesSeniorLead💰 $170,144 - $212,750 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and deploying scalable AI/ML solutions in cloud environments, with a strong focus on MLOps best practices, infrastructure management, and compliance with federal standards. Proficient in utilizing advanced machine learning frameworks and tools to optimize model performance and ensure responsible AI practices.

Highest-signal resume keywords

AI/ML Architecture DesignMLOps Best PracticesCloud Environment DeploymentPython ProgrammingInfrastructure-as-Code

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI/ML Pipeline DevelopmentModel TrainingFeature EngineeringModel ValidationTensorFlowPyTorchDockerKubernetesTerraformAmazon SageMaker

Soft Skills

Technical LeadershipStrategic Direction

Tools & Technologies

Azure Machine LearningGoogle Cloud Vertex AIMLflowKubeflowAirflow

Certifications & Qualifications

AWS Certified Machine Learning Engineer – AssociateMicrosoft Certified: Azure AI Apps and Agents Developer Associate

Industry Keywords

CMM ProgramNIST 800-53FedRAMPMulti-Tenant SystemsCloud-Native Architecture

Tech Stack

Tools & technologies

AirflowAWSAzureCloudDistributed SystemsDockerKerasKubernetesPythonPyTorchScikit-LearnTensorflowTerraform

About the role

Key responsibilities & impact

Define, document, and maintain a scalable, modular AI/ML architecture aligned with enterprise cloud strategy and CMM Program requirements
Architect and implement end-to-end AI/ML pipelines covering data ingestion, feature engineering, model training, deployment, monitoring, and retraining
Integrate MLOps best practices for continuous integration, delivery, and machine-learning model lifecycle management
Ensure multi-tenant and multi-region AI/ML workloads are elastic, highly available, and cost-efficient
Use Infrastructure-as-Code tools to provision and manage cloud-based AI/ML infrastructure in accordance with federal compliance and security standards
Design and support reusable, secure, and performant AI/ML services and APIs for enterprise applications
Conduct model validation and optimization in cloud environments while ensuring responsible AI, fairness, and transparency
Maintain AI/ML architecture documentation and update artifacts per sprint cycles or as changes occur
Support AI/ML metrics, resource utilization, and performance KPIs in technical dashboards and reports
Guide teams in selecting and securely integrating third-party ML tools, frameworks, and SaaS offerings
Provide technical leadership, strategic direction, and tool recommendations for AI/ML architecture
Support the Case Management Modernization Program for the Administrative Office of the US Courts and 204+ federal courts

Requirements

What you’ll need

BA/BS or equivalent required; Master of Arts or Science in a related field preferred
15+ years of related experience required
At least 5+ years of recent, demonstrable, hands-on experience architecting and deploying AI/ML workflows in cloud environments
Experience with Amazon SageMaker AI, Azure Machine Learning, and/or Google Cloud Vertex AI
Hands-on experience with TensorFlow, PyTorch, scikit-learn, XGBoost, or Keras
Strong Python programming skills
Experience with Docker and Kubernetes for ML workloads
Experience with MLflow, Kubeflow, TFX, or Airflow and CI/CD integration for model deployment
Familiarity with JISF, NIST 800-53, and FedRAMP
Proficiency with Terraform, CDK, or CloudFormation
Experience with multi-tenant distributed systems and cloud-native architecture patterns
Must be able to pass a background check to obtain a position of Public Trust
Must be a US Person (Green Card Holder, US Permanent Resident Alien, Refugee, Asylee, or US Citizen)
Preferred but not required certifications: AWS Certified Machine Learning Engineer – Associate; AWS Certified Machine Learning – Specialty; Microsoft Certified: Azure AI Apps and Agents Developer Associate; Microsoft Certified: Machine Learning Operations Engineer Associate; Google Cloud Professional Machine Learning Engineer

Benefits

Comp & perks

Comprehensive medical plan options, including some with Health Savings Accounts
Dental plan options
Vision plan options
401(k) plan with pre-tax and post-tax contributions and company match
Full flex work weeks where possible
Paid vacation, sick, personal, holiday, parental, military, bereavement, and jury duty leave
Typically 15 days of paid leave per calendar year
10 paid holidays per year
Up to 160 hours of paid family leave in a rolling 12-month period for eligible employees
Short- and long-term disability benefits
Life insurance
Accidental death and dismemberment insurance
Personal accident insurance
Critical illness insurance
Business travel and accident insurance
AI-powered career tool identifying career steps and learning opportunities
Internal mobility team supporting career goals
Growth opportunities
Award-winning culture of innovation
Military-friendly workplace