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FTI Defense delivers mission-focused solutions to the Department of Defense and Intelligence Community through advanced engineering, digital transformation, and program execution expertise. We help our customers solve complex challenges and achieve mission success by integrating people, process, and technology.

FTI Defense is seeking a Distinguished AI/ML Engineer to serve as a technical leader, architect, and integrator — designing, building, deploying, and sustaining AI systems that transform complex mission data into trusted, explainable insights.

This is a hands-on builder role, not an analytics management position. The ideal candidate is equally comfortable writing model code, standing up ML pipelines, and integrating AI inference services into operational systems within secure environments. The right candidate blends deep AI/ML engineering expertise with system-level architecture leadership and an ability to unify data engineering, simulation modeling, and responsible AI principles into scalable, mission-ready capabilities.

• 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.

• Active Secret clearance required; TS/SCI strongly preferred.
• Bachelor’s degree in Computer Science, Engineering, or a related technical field (Master’s or Ph.D. preferred).
• 10+ years of overall experience in AI/ML development, with 5+ years designing and deploying scalable AI/ML architectures, including at least two full lifecycle implementations (from prototype to operational system).
• Proficiency in Python, PyTorch, TensorFlow, and modern ML frameworks.
• Experience designing or deploying systems using vector databases (Milvus, Pinecone, Weaviate), knowledge graphs, and semantic search frameworks.
• Proven ability to design event-driven data pipelines using Databricks, Spark, Flink, or Kafka.
• Demonstrated experience deploying AI/ML systems in secure, classified, or edge environments.
• Familiarity with Responsible AI and assurance principles, including bias detection, explainability, human-machine teaming, and hallucination prevention.
• Experience integrating AI models into simulation, modeling, or operational planning systems is highly desirable.
• Experience transitioning R&D systems into accredited production environments.
• Strong communication and mentoring skills, with the ability to lead technically while remaining deeply hands-on.

For this role, the compensation range for candidates is $200K-225k. *Note: Starting pay will be based on a number of factors and commensurate with qualifications & experience. FTI has a location-based compensation structure; there may be a different range for candidates in other locations.

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