AI Engineer - Entry to Expert Level (Maryland)

We need to output HTML fragment with sections: Responsibilities, Job Summary, Qualifications, Pay, Benefits, Work Schedule (maybe separate sections). According to rules: Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use

• for any list of duties/requirements/benefits, and

  for prose. Do NOT repeat the job title as a heading. Do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

  or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Prefer a few substantive sections over many thin ones. We need to extract substantive content: Responsibilities (list), Job Summary (paragraph), Qualifications (detailed text with bullet-like paragraphs). Pay, Benefits, Work Schedule sections: The posting includes "Pay, Benefits, & Work Schedule" with details: Pay: Salary offers based on education and experience, salary range $87,362 - $197,200 (Entry/Developmental, Full Performance, Senior, Expert). Salary range varies by location, work level, and relevant experience. 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). Also DCIPS Trial Period details. We need to decide sections: Could have Pay, Benefits, Work Schedule as separate sections or combine? The instruction says prefer a few substantive sections over many thin ones. Could combine Pay, Benefits, Work Schedule into one section? But they said use headings for Pay, Benefits, Schedule. However they also say include a section ONLY when posting has real, specific content for it. The posting does have content for Pay, Benefits, Work Schedule. So we could create three sections: Pay, Benefits, Schedule. Or we could combine them into one "Pay, Benefits, & Work Schedule" section but heading must be one of the allowed headings: Pay, Benefits, Schedule. The allowed headings list includes Pay, Benefits, Schedule. So we could have three separate sections. We need to avoid filler like "Salary range varies by location, work level, and relevant experience." That's substantive. "Training will be provided based on the selectee's needs and experience." That's also substantive. Benefits: "NSA offers a comprehensive benefits package." That's vague but still substantive? It's a statement; not placeholder like "commensurate with experience". It's okay. Work Schedule: details given. Also DCIPS Trial Period details: Should we include under Work Schedule or maybe a separate section? Not in allowed headings; could be part of Work Schedule as prose. We must remove application instructions, How To Apply, etc. Also remove EEO, etc. We need to preserve all substantive information: Responsibilities list, Job Summary paragraph, Qualifications (including the long text about degrees, entry, full performance, senior, expert). That's a lot; we need to preserve it but format as

  prose? The qualifications section includes lists of coursework, etc. Could we turn some into

  • ? The rule: Use
    • for any list of duties/requirements/benefits. The qualifications includes lists like "Specified coursework includes courses in differential and integral calculus and 5 of the following 18 areas: (a) statics or dynamics, ..." That's a list; we could make a
      • . Also the entry requirements for different degree fields: could be a list. But we must preserve all info; we can convert to lists for readability but must not lose info. We need to decide structure:

        Responsibilities

        then
        • each responsibility. Then

          Job Summary

          then

          paragraph(s). Then

          Qualifications

          then maybe multiple

          or

          for subparts. Then

          Pay

          then

          with salary details. Then

          Benefits

          then

          . Then

          Schedule

          then

          with work schedule and DCIPS trial period. We must not include headings for empty sections. Now ensure we don't include any boilerplate like "U.S. Citizenship is required for all applicants." That's actually substantive? It's a requirement; could be part of Qualifications? It's a requirement but not listed under qualifications; it's a separate statement. Should we preserve? It's substantive info about eligibility. The instruction says preserve ALL substantive information; do not invent or summarize away detail. So we should include that somewhere. Where? Could be under Qualifications as an additional requirement, or maybe a separate section like "Requirements". But we have no heading for Requirements? Allowed headings include Requirements. Could add a

          Requirements

          for citizenship, security clearance, drug testing, polygraph, etc. That would be substantive. However we must avoid creating thin sections; but we have enough content for Requirements: US citizenship, security background investigation, polygraph, random drug testing, reasonable accommodations. That's substantive. We could also include "Equal opportunity employer" etc but that's boilerplate; remove. Thus we may have sections: Responsibilities, Job Summary, Qualifications, Requirements, Pay, Benefits, Schedule. Check if any of those sections would be just filler: Requirements will have concrete info. Now we need to extract the exact text for each. Let's copy the responsibilities bullet points: - 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. Job Summary paragraph: "As an AI Engineer at NSA, you will design and implement mission-critical AI systems that keep the agency at the cutting edge of intelligence collection, processing and reporting to keep the nation safe. You'll apply your expertise in data science, plus software, data and systems engineering to build, deploy and maintain AI systems at scale while addressing the entire AI life cycle, including infrastructure management, efficient model training, production deployment, performance monitoring and continuous optimization. Depending on your experience level, you will be assigned to a mission office or enrolled in the three-year Data Science Development Program, which will both broaden and specialize your AI engineering skills through courses and working in a variety of mission offices (each for several months). In either case, you will work with NSA experts in AI engineering, related technical domains and specialized subject areas. You'll have opportunities to participate in internal technical roundtables and attend technical conferences with experts from industry and academia." Qualifications: The 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