AI Engineer

About This Role

As an AI Engineer at NSA, you will design, develop, deploy, and maintain mission-critical AI systems to advance intelligence collection, processing, and reporting. This role involves applying modern engineering techniques and adapting diverse AI model architectures to solve complex national security challenges.

Responsibilities

• Lead or contribute to cross-functional teams to develop and operationalize AI solutions.
• Apply modern engineering techniques to design, develop, deploy, and maintain end-to-end AI workflows.
• Adapt and integrate diverse AI model architectures (computer vision, NLP, audio, LLMs, multi-modal frameworks) to address complex mission-critical challenges.
• Monitor and maintain AI products through systematic identification of performance degradation and computational inefficiency.
• Address performance and efficiency challenges through regular retuning and fine-tuning AI models.
• Maintain knowledge of current AI research and adapt emerging techniques to intelligence applications.
• Test and evaluate AI solutions against mission requirements and produce actionable recommendations.

Requirements

• Ability to complete the Data Science Examination (DSE)
• Associate's degree plus 2 years of relevant experience, or a Bachelor's degree and no experience, or a Master's degree and no experience in Computer Science or Engineering OR Associate's degree plus 3 years of relevant experience, or a Bachelor's degree and 1 year of relevant experience in Information Systems, IT, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, AI, Data Science, or Physical or Biological Sciences.
• Experience in implementing production scale AI/ML solutions, distributed model training, distributed AI/ML systems, AI/ML performance monitoring, platform engineering, cloud engineering, developing deep learning models, neural networks, sustaining/maintaining AI/ML models, implementing AI/ML algorithms, AI/ML model development and deployment, DevOps, MLOps, cloud infrastructure management, software engineering, automated testing, or containerization.

Qualifications

• Associate's degree plus 2-11 years of experience, Bachelor's degree and 0-9 years of experience, Master's degree and 0-7 years of experience, or Doctoral degree and 0-5 years of experience in Computer Science, Engineering, Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Artificial Intelligence, Data Science, or Physical or Biological Sciences.
• Entry is with an Associate's degree plus 2 years of relevant experience, or a Bachelor's degree and no experience, or a Master's degree and no experience in Computer Science or engineering. Relevant experience must be in one or more of the following: implementing production scale AI/ML, distributed model training, distributed AI/ML systems, AI/ML performance monitoring, platform engineering, cloud engineering, developing deep learning models, neural networks, sustaining/maintaining AI/ML models, implementing AI/ML algorithms, AI/ML model development and deployment, DevOps, MLOps, cloud infrastructure management, software engineering, automated testing, or containerization.

Nice to Have

• Deep learning frameworks (PyTorch, TensorFlow, Jax)
• Model training, fine-tuning, and optimization techniques
• Computer vision, NLP, speech/audio processing, and/or multi-modal AI systems
• Large language models (LLMs) and transformer architectures
• Model evaluation, validation, and performance monitoring
• Transfer learning and domain adaptation
• Python programming and other relevant languages (C++, Java, Scala, TypeScript)
• Version control (Git) and collaborative development
• API design and microservices architecture
• Software testing frameworks and CI/CD pipelines
• Containerization (Docker, Kubernetes)
• Data processing frameworks (Spark, Dask, Ray)
• Feature engineering and data preprocessing
• Production model deployment and serving infrastructure
• Monitoring, logging, and observability tools
• Cloud platforms (AWS, Azure, GCP) and/or HPC systems
• Distributed computing and parallel processing
• GPU optimization and resource management
• Database systems (SQL and NoSQL)
• Cross-function collaboration and communication
• Technical documentation and presentation
• Ability to translate mission requirements into technical solutions

Skills

* Required skills

Benefits