AI/ML Specialist

Designs, develops, and modifies AI applications, tools, and/or other solutions to enable successful accomplishment of mission objectives.

Tasks

The concrete work activities defined for this role in the DCWF v5.1 spreadsheet. Core tasks are required for the role; additional tasks are associated but not mandatory.

• T1000B additional Ensure that AI design and development activities are properly documented and updated.
• T477 additional Correct errors by making appropriate changes and rechecking the program to ensure desired results are produced.
• T506 additional Design, develop, and modify software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design.
• T5120 additional Conduct hypothesis testing using statistical processes.
• T543 additional Develop secure code and error handling.
• T5847 additional Assess and address the limitations of methods to deliver machine learning models.
• T5854 additional Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability concerns for AI solutions.
• T5858 additional Conduct AI risk assessments to ensure models and/or other solutions are performing as designed.
• T5859 additional Consider energy implications (graphical processing unit, tensor processing unit, etc.) when designing AI solutions.
• T5870 additional Design and develop continuous integration/continuous delivery (CI/CD) in a containerized or other reproducible computing environment to support the machine learning life cycle.
• T5871 additional Design and develop machine learning models to achieve organizational objectives.
• T5872 additional Design, develop, and implement AI tools and techniques to achieve organizational objectives.
• T5873 additional 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.
• T5889 additional Identify and submit exemplary AI use cases, best practices, failure modes, and risk mitigation strategies, including after-action reports.
• T5893 additional 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.
• T5896 additional Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.
• T5915 additional Research the latest machine learning and AI tools, techniques, and best practices.
• T5925 additional Use knowledge of business processes to create or recommend AI solutions.
• T5926 additional Use models and other methods for evaluating AI performance.
• T5927 additional Write and document reproducible code.
• T764 additional Perform secure programming and identify potential flaws in codes to mitigate vulnerabilities.

Knowledge, Skills, and Abilities

KSA statements define what a person filling this role knows or can do. "Knowledge" is what they must know, "Skill" is what they can perform, and "Ability" is a durable capacity they bring to the work.

• A6060 ability core Ability to collect, verify, and validate test data.
• K0021 knowledge core Knowledge of computer algorithms.
• K0102 knowledge core Knowledge of programming language structures and logic.
• K075A knowledge core Knowledge of mathematics, including logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis.
• K6311 knowledge core Knowledge of machine learning theory and principles.
• K7009 knowledge core Knowledge of coding and scripting in languages that support AI development and use.
• K7011 knowledge core Knowledge of current AI and machine learning systems design and performance analysis models, algorithms, and tools.
• K7020 knowledge core Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).
• K7024 knowledge core Knowledge of how AI is developed and operated.
• K7028 knowledge core Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software.
• K7029 knowledge core Knowledge of how to collect, store, and monitor data.
• K7031 knowledge core Knowledge of how to structure and display data.
• K7032 knowledge core Knowledge of how to use data to tell a story.
• K7037 knowledge core Knowledge of machine learning operations (MLOps) processes and best practices.
• K7038 knowledge core Knowledge of metrics to evaluate the effectiveness of machine learning models.
• K7045 knowledge core Knowledge of the AI lifecycle.
• K7046 knowledge core Knowledge of the basic requirements for the successful delivery of AI solutions.
• K7048 knowledge core Knowledge of the benefits and limitations of AI capabilities.
• K7049 knowledge core Knowledge of the latest machine learning and AI tools, techniques, and best practices.
• K7050 knowledge core Knowledge of the nature and function of technology platforms and tools used to create and employ AI.
• K7051 knowledge core Knowledge of the possible impacts of machine learning blind spots and edge cases.
• S166 skill core Skill in conducting queries and developing algorithms to analyze data structures.
• S6760 skill core Skill in writing scripts using R, Python, PIG, HIVE, SQL, etc.
• S7055 skill core Skill in analyzing the output from machine learning models.
• S7057 skill core Skill in building and deploying machine learning models.
• S7059 skill core Skill in creating machine learning models.
• S7065 skill core Skill in explaining AI concepts and terminology.
• S7067 skill core Skill in identifying low-probability, high-impact risks in machine learning training data sets.
• S7075 skill core Skill in testing and evaluating machine learning algorithms and/or AI solutions.
• K0942 knowledge additional Knowledge of the organization's core business/mission processes.
• K6290 knowledge additional Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems.
• K7003 knowledge additional Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions.
• K7021 knowledge additional Knowledge of emerging trends and future use cases of AI.
• K7022 knowledge additional Knowledge of how AI adoption can assist developers with service-oriented design.
• K7025 knowledge additional Knowledge of how AI solutions integrate with cloud or other IT infrastructure.
• K7026 knowledge additional Knowledge of how commercial and federal solutions solve Defense-related data environment and platform challenges.
• K7036 knowledge additional Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.
• K7040 knowledge additional Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions.
• K7041 knowledge additional Knowledge of remedies against unintended bias in AI solutions.
• K7044 knowledge additional Knowledge of testing, evaluation, validation, and verification (T&E V&V) tools and procedures to ensure systems are working as intended.
• S7069 skill additional Skill in identifying risk over the lifespan of an AI solution.
• S7071 skill additional Skill in labeling data to make it more discoverable and understandable.

Other roles in this element