• 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. • Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs. • Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems. • Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.

• Responsible for researching, designing, developing, and implementing AI solutions that enhance Identity, Credential, and Access Management (ICAM) capabilities • Collaborate with identity architects, cybersecurity engineers, data analysts, software developers, and mission stakeholders • Identify opportunities where AI can improve identity analytics, access governance, operational efficiency, cybersecurity, and decision support • Support the full lifecycle of AI initiatives from concept development and experimentation through production implementation and sustainment • Analyze large volumes of authentication, authorization, audit, entitlement, and access governance data to identify trends, risks, and actionable insights • Collaborate with engineers and architects to integrate AI capabilities into existing ICAM platforms and enterprise services

• Define and drive the technical strategy for the agentic platform • Frame and prioritize the highest-impact AI problems • Establish the evaluation and quality framework for AI systems • Set the architecture for trust • Lead the design and delivery of large, cross-team initiatives • Drive adoption of shared agentic capabilities • Act as technical authority for critical decisions and trade-offs • Mentor senior engineers and partner with Product/UX/Applied AI on long-term roadmap

• Implement and maintain clean, well-tested code for MCP tools, agentic features, and their supporting services (front-end and back-end), to team standards • Integrate LLMs and models via APIs/SDKs to deliver agentic workflows, with attention to correctness, latency, and cost • Help build the guardrails of agentic workflows — human-in-the-loop steps, traceability, and clear error handling • Contribute to automated tests, evaluation harnesses for AI behavior, CI/CD pipelines, and developer tooling • Debug and troubleshoot across the stack (agent/tool logic, APIs, web UI), collaborating with senior engineers and QA • Work with Product and UX to translate requirements into technical tasks; surface risks and blockers early and seek mentorship