CFD Research is seeking a Senior AI/ML Engineer, to design, build, and deploy advanced artificial intelligence and machine learning solutions that modernize mission systems and accelerate decision‑making across the national security ecosystem. The candidate will contribute end‑to‑end-spanning data processing, model development, MLOps, and integration of scalable AI capabilities into operational environments.

The work will leverage deep experience with ML‑based NLP, LLMs and generative AI, computer vision, and modern software engineering practices, consistent with the firm’s AI/ML engineering role definitions. We value first-principles reasoning and physically interpretable models, so experience developing hybrid systems that combine calibrated sensor models, deterministic algorithms, probabilistic inference, and learned components is particularly desirable. Candidates should be motivated by building systems that are scientifically explainable, empirically validated, and capable of operating robustly across both synthetic and real-world sensing environments. You will partner closely with cross‑functional engineering, mission SMEs, and product teams to deliver production‑grade systems aligned with Booz Allen’s AI modernization approaches.

• Design, develop, and implement AI/ML models-including NLP, LLM‑based pipelines, deep learning architectures, and computer vision models-aligned to mission requirements
• Build and optimize scalable data pipelines to support training, evaluation, and continuous improvement of models
• Develop end‑to‑end production systems including microservices, APIs, automation frameworks, and cloud‑native or edge‑deployed AI services
• Integrate AI/ML capabilities into operational environments with attention to security, reliability, observability, and performance
• Conduct model optimization, tuning, versioning, and AB testing to ensure robust mission impact
• Apply modern software engineering patterns (Python, Java, Scala, Rust; microservices; distributed systems) as consistent with Booz Allen’s AI/ML engineering frameworks
• Collaborate with multi‑disciplinary teams to identify mission needs, gather requirements, and define technical approaches
• Prepare technical documentation, contribute to design reviews, and support accreditation/security processes as required in classified environments

Basic Qualifications:

• Experience in GPU programming, including CUDA or RAPIDs
• Experience with AWS and containerization of models in a classified environment 
• Experience with LLM orchestration, RAG pipelines, grounding, or hallucination‑mitigation workflows
• Experience with MLOps platforms, distributed training, or hybrid cloud environments (AWS, Azure, GCP)
• Familiarity with automated testing tools and model evaluation frameworks
• Master’s degree in Computer Science, AI/ML Engineering, Data Science, or related STEM field
• Industry certifications in AI/ML, cloud, or solution architecture

• 5+ years of experience developing machine learning models (such as: NLP/LLM, CV, deep learning, generative AI, reinforcement learning, or other related)

• 5+ years experience developing AI/ML capabilities to operate in a microservice stack
• Experience developing or maintaining production‑grade APIs, microservices, or distributed systems
• Experience with data processing frameworks and scientific computing libraries (such as: NumPy, Pandas, PyTorch, TensorFlow, or scikit‑learn)
• Experience designing, configuring, or deploying software systems in operational environments
• Experience with CI/CD tooling, source control (such as GitLab) and containerization technologies (such as: Docker, Podman, or Kubernetes)
• U.S. Citizenship is REQUIRED. 
• ACTIVE TS/SCI clearance IS REQUIRED, and willingness to take a polygraph
• Bachelor's degree in a related STEM field

Desired Qualifications:

• Experience in GPU programming, including CUDA or RAPIDs
• Experience with AWS and containerization of models in a classified environment 
• Experience with LLM orchestration, RAG pipelines, grounding, or hallucination‑mitigation workflows
• Experience with MLOps platforms, distributed training, or hybrid cloud environments (AWS, Azure, GCP)
• Familiarity with automated testing tools and model evaluation frameworks
• Master’s degree in Computer Science, AI/ML Engineering, Data Science, or related STEM field
• Industry certifications in AI/ML, cloud, or solution architecture