AI Engineer - Entry to Expert Level (Maryland)

AI Engineers will:
- Lead or contribute to cross-functional teams to develop and operationalize AI solutions that help solve our most challenging problems.
- Apply modern engineering techniques to design, develop, deploy and maintain end-to-end AI workflows spanning model training, inference and performance monitoring.
- Adapt and integrate diverse AI model architectures, including computer vision systems, natural language processors, audio processors, large language models (LLMs) and multi-modal frameworks to address complex mission-critical challenges.
- Monitor and maintain AI products through systematic identification of performance degradation and computational inefficiency and address these challenges through regular fine-tuning to ensure continued alignment with evolving mission needs and organizational goals.
- 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.

Conditions of employment

• All applicants and employees are subject to random drug testing in accordance with Executive Order 12564.

Qualifications

ENTRY
Note that different degree fields have different requirements as described below. For degrees in Computer Science or Engineering, 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. For degrees in 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 3 years of relevant experience, or a Bachelor's degree and 1 year of relevant experience. Relevant experience must be in one or more of the following: implementing production scale AI/ML (Artificial Intelligence / Machine Learning) 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.

FULL PERFORMANCE Note that different degree fields have different requirements as described below. For degrees in Computer Science or Engineering, entry is with an Associate's degree plus 5 years of relevant experience, or a Bachelor's degree plus 3 years of relevant experience, or a Master's degree plus 1 year of relevant experience, or a Doctoral degree and no experience. For degrees in 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 5 years of relevant experience, or a Bachelor's degree plus 3 years of relevant experience, or a Master's degree plus 1 year of relevant experience, or a Doctoral degree and 1 year of relevant experience. Relevant experience must be in one or more of the following: 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, 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.

SENIOR
Entry is with an Associate's degree plus 8 years of relevant experience, or a Bachelor's degree plus 6 years of relevant experience, or a Master's degree plus 4 years of relevant experience, or a Doctoral degree plus 2 years of relevant experience. Degree must be 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. Relevant experience must be in two or more of the following: 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, 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.

EXPERT
Entry is with an Associate's degree plus 11 years of relevant experience, or a Bachelor's degree plus 9 years of relevant experience, or a Master's degree plus 7 years of relevant experience, or a Doctoral degree plus 5 years of relevant experience. Degree must be 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. Relevant experience must be in three or more of the following: 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, 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. Additionally, you must have experience in serving as an AI Project Team Leader/model owner.

How you will be evaluated

You will be evaluated for this job based on how well you meet the qualifications above.

Specialized skills and experience in one or more of the following is desired:
- 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

The National Security Agency (NSA) is part of the DoD Intelligence Community Defense Civilian Intelligence Personnel System (DCIPS). All positions in the NSA are in the Excepted Services under 10 United States Codes (USC) 1601 appointment authority. DoD Components with DCIPS positions apply Veterans' Preference to eligible candidates as defined by Section 2108 of Title 5 USC, in accordance with the procedures provided in DoD Instruction 1400.25, Volume 2005, DCIPS Employment and Placement. If you are a veteran claiming veterans' preference, as defined by Section 2108 of Title 5 U.S.C., you may be asked to submit documents verifying your eligibility.

Next steps