AI/ML Architect SME

Type of Requisition:RegularClearance Level Must Currently Possess:NoneClearance Level Must Be Able to Obtain:NonePublic Trust/Other Required:BI Full 6C (T4)Job Family:Data Science and Data EngineeringJob Qualifications:Skills:Artificial Intelligence (AI), Cloud Development, Machine Learning (ML)Certifications:NoneExperience:15 + years of related experienceUS Citizenship Required:NoJob Description:Seize your opportunity to make a personal impact supporting the Case Management Modernization (CMM) Program. The CMM Program is an initiative to support the Administrative Office of the US Courts (AO) in developing a modern, cloud-based solution to support all 204+ federal courts across the United States. GDIT is your place to make meaningful contributions to challenging projects and grow a rewarding career. The AI/ML Architect SME will be responsible for defining, designing, deploying, and maintaining the modular AI/ML architecture necessary for the CMM Program. They will architect and implement end-to-end AI/ML pipelines that include data ingestion, feature engineering, model training, deployment, monitoring, and retraining, ensuring the continuous effectiveness and improvement of these solutions. This role provides technical leadership, strategic direction, and tool recommendations for AI/ML architecture to improve the architecture, automation, and delivery of AI/ML products. We are seeking a candidate with recent, demonstrable, hands-on experience in the AI/ML field to play a key role in the development of this exciting modernization effort that will have far-reaching impacts across the full breadth of the US federal courts system. If you’re interested in this exciting opportunity and would like to grow your AI/ML career with a company that always puts its people at the forefront of everything we do, then we’d love to hear from you!RESPONSIBILITIES: Define, document, and maintain a scalable, modular AI/ML architecture that aligns with enterprise cloud strategy and product requirements for the CMM Program. Architect and implement end-to-end AI/ML pipelines including data ingestion, feature engineering, model training, deployment, monitoring, and retraining. Integrate MLOps best practices for continuous integration, delivery, and lifecycle management of machine learning models Ensure multi-tenant and multi-region AI/ML workloads are elastic, highly available, and cost-efficient Leverage Infrastructure-as-Code (IaC) 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 to be consumed by enterprise applications Conduct model validation and optimization in cloud environments, ensuring responsible AI practices, fairness, and transparency Maintain AI/ML architecture documentation and update artifacts per sprint cycles or as changes occur Support the inclusion of AI/ML metrics, resource utilization, and performance KPIs in technical dashboards and reports Guide teams on the selection and integration of third-party ML tools, frameworks, or SaaS offerings in a secure and compliant manner REQUIRED EXPERIENCE & QUALIFICATIONS: Education: BA/BS or equivalent required; Master of Arts or Science in related field preferred Experience: 15+ years of related experience required, with at least 5+ years of recent, demonstrable, hands-on experience architecting and deploying AI/ML workflows in cloud environments utilizing Amazon SageMaker AI, Azure Machine Learning, and/or Google Cloud Vertex AI Experience may be considered in lieu of degree as follows: HS (19+ years), AA/AS (17+ years), BA/BS (15+ years), MA/MS (13+ years), Doctorate Degree/Ph.D. (12+ years) Proven success in architecting and deploying AI/ML workflows in cloud environments (Amazon SageMaker AI, Azure Machine Learning, and/or Google Cloud Vertex AI) Hands-on experience with ML/DL frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost, or Keras Strong programming skills in Python, with experience in containerization (Docker) and orchestration (Kubernetes) for ML workloads Experience with MLOps tools (MLflow, Kubeflow, TFX, Airflow) and CI/CD integration for model deployment Familiarity with federal data governance, security, and privacy standards, including JISF, NIST 800-53, and FedRAMP Proficient in using IaC tools such as Terraform, CDK, or CloudFormation to automate cloud ML infrastructure Experience with multi-tenant, distributed systems, and cloud-native architecture patterns Preferred Certification(s):One or more of the following certifications is preferred but not required. 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 Security Clearance Level: Must be able to pass a ba