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.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 solutionsThe qualifications listed are the minimum acceptable to be considered for the position.

For all of the Engineering degrees, if program is not ABET accredited, it must include specified coursework.*

• Specified coursework includes courses in differential and integral calculus and 5 of the following 18 areas: (a) statics or dynamics, (b) strength of materials/stress-strain relationships, (c) fluid mechanics, hydraulics, (d) thermodynamics, (e) electromagnetic fields, (f) nature and properties of materials/relating particle and aggregate structure to properties, (g) solid state electronics, (h) microprocessor applications, (i), computer systems, (j) signal processing, (k) digital design, (l) systems and control theory, (m) circuits or generalized circuits, (n) communication systems, (o) power systems, (p) computer networks, (q) software development, (r) Any other comparable area of fundamental engineering science or physics, such as optics, heat transfer, or soil mechanics.

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.Pay: Salary offers are based on candidates' education level and years of experience relevant to the position and also take into account information provided by the hiring manager/organization regarding the work level for the position.

Salary Range: $87,362 - $197,200 (Entry/Developmental, Full Performance, Senior, Expert) Salary range varies by location, work level, and relevant experience to the position.

Training will be provided based on the selectee's needs and experience.

Benefits: NSA offers a comprehensive benefits package.

Work Schedule: This is a full-time position, Monday - Friday, with basic 8hr/day work requirement between 6:00 a.m. and 6:00 p.m. (flexible).

DCIPS Trial Period: If selected for this position, you will be required to serve a two-year DCIPS trial period, unless you are a veterans' preference-eligible employee, in which case you are required to serve a one-year trial period. This trial period runs concurrently with your commitment to the position, if applicable. Before finalizing your appointment at the conclusion of your trial period, NSA will determine whether your continued employment advances the public interest. This decision will be based on factors such as your performance and conduct; the Agency's needs and interests; whether your continued employment would advance the Agency's organizational goals; and whether your continued employment would advance the efficiency of the Federal service.

Upon completion of your trial period, your employment will be terminated unless you receive certification, in writing, that your continued employment advances the public interest.

If you do not receive certification for continued employment, you should receive written notice prior to the end of your trial period that your employment will be terminated and the effective date of such termination.