• Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines. • Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems. • Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud). • Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs. • Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems. • Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection. • Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations. • Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems.