Knowledge of computer algorithms.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

Knowledge of mathematics, including logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis.

Knowledge of programming language structures and logic.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Skill in conducting queries and developing algorithms to analyze data structures.

Correct errors by making appropriate changes and rechecking the program to ensure desired results are produced.

Design, develop, and modify software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design.

Develop secure code and error handling.

Perform secure programming and identify potential flaws in codes to mitigate vulnerabilities.

Ensure that AI design and development activities are properly documented and updated.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Conduct hypothesis testing using statistical processes.

Assess and address the limitations of methods to deliver machine learning models.

Conduct AI risk assessments to ensure models and/or other solutions are performing as designed.

Design and develop machine learning models to achieve organizational objectives.

Design, develop, and implement AI tools and techniques to achieve organizational objectives.

Determine methods and metrics for quantitative and qualitative measurement of AI risks so that sensitivity, specificity, likelihood, confidence levels, and other metrics are identified, documented, and applied.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Research the latest machine learning and AI tools, techniques, and best practices.

Use models and other methods for evaluating AI performance.

Write and document reproducible code.

Ability to collect, verify, and validate test data.

Knowledge of machine learning theory and principles.

Skill in writing scripts using R, Python, PIG, HIVE, SQL, etc.

* Knowledge of specific operational impacts of cybersecurity lapses.

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Knowledge of coding and scripting in languages that support AI development and use.

Knowledge of current AI and machine learning systems design and performance analysis models, algorithms, and tools.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of how AI is developed and operated.

Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software to the DoD.

Knowledge of how to collect, store, and monitor data.

Knowledge of how to structure and display data.

Knowledge of how to use data to tell a story.

Knowledge of machine learning operations (MLOps) processes and best practices.

Knowledge of metrics to evaluate the effectiveness of machine learning models.

Knowledge of the AI lifecycle.

Knowledge of the basic requirements for the successful delivery of AI solutions.

Knowledge of the benefits and limitations of AI capabilities.

Knowledge of the latest machine learning and AI tools, techniques, and best practices.

Knowledge of the nature and function of technology platforms and tools used to create and employ AI.

Knowledge of the possible impacts of machine learning blind spots and edge cases.

Skill in analyzing the output from machine learning models.

Skill in building and deploying machine learning models.

Skill in creating machine learning models.

Skill in explaining AI concepts and terminology.

Skill in identifying low-probability, high-impact risks in machine learning training data sets.

Skill in testing and evaluating machine learning algorithms or AI solutions.

Knowledge of the organization’s core business/mission processes.

Use knowledge of business processes to create or recommend AI solutions.

Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions.

Consider energy implications (graphical processing unit, tensor processing unit, etc.) when designing AI solutions.

Design and develop continuous integration/continuous delivery (CI/CD) in a containerized or other reproducible computing environment to support the machine learning life cycle.

Identify and submit exemplary AI use cases, best practices, failure modes, and risk mitigation strategies, including after-action reports.

Implement Responsible AI best practices and standards within AI solutions according to the DoD AI Ethical Principles, Responsible AI Guidelines, and/or any other pertinent laws.

Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems.

Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions.

Knowledge of emerging trends and future use cases of AI.

Knowledge of how AI adoption can assist developers with service-oriented design.

Knowledge of how AI solutions integrate with cloud or other IT infrastructure.

Knowledge of how commercial and federal solutions solve Defense-related data environment and platform challenges.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions.

Knowledge of remedies against unintended bias in AI solutions.

Knowledge of testing, evaluation, validation, and verification (T&E V&V) tools and procedures to ensure systems are working as intended.

Skill in identifying risk over the lifespan of an AI solution.

Skill in labeling data to make it more discoverable and understandable.