AI/ML Engineer

Role Description

FTI Defense is seeking a hands-on AI/ML Engineer to design, build, and deploy advanced machine learning solutions supporting defense and national security missions. This role focuses on execution in oversight, ideal for an engineer who thrives in the code, enjoys building end-to-end pipelines, and takes pride in seeing their work directly impact operational systems.

Responsibilities include:

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.
• Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.
• Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration).
• Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).
• Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.
• Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).
• Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy.
• Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots.
• Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs.
• Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.

Qualifications

• Must be a U.S. citizen and be willing to obtain and maintain a security clearance, as needed.
• 6-10+ years of professional experience developing and deploying AI/ML solutions in production environments.
• Minimum of 3 years' professional experience within the Department of Defense/Department of War (DoD/DoW).
• Strong Python development skills with hands-on experience building AI/ML solutions.
• Direct experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain.
• Proven ability to build and deploy MLOps pipelines using MLflow, Kubeflow, DVC, or equivalent.
• Working knowledge of vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval-based architectures (RAG, hybrid, graph).
• Professional experience fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT.
• Professional experience integrating AI capabilities into production systems or mission applications.

Requirements

• Familiarity with agentic frameworks (LangGraph, AutoGen, CrewAI, DSPy) and multi-agent reasoning.
• Understanding of prompt engineering, retrieval quality, and grounding methods.
• Exposure to GPU-based or edge inference environments.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.
• Active Secret clearance preferred; ability to obtain one is required.