Senior AI/ML Engineer

CFD Research Corporation•Dayton, OH

About The Position

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. The company values 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.

Requirements

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

Nice To Haves

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

Responsibilities

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