AI Foundations, Early Practitioner
Freedom
contract
Posted on:
Location Type: Remote
Location: United States
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About the role
- Complete a practitioner-level skills assessment used for validation and standard-setting purposes.
- Complete a short post-assessment survey providing feedback on the assessment experience.
Requirements
- Candidates should be a current practitioner with applied, real-world experience related to the following knowledge areas and skills:
- Define artificial intelligence and describe its core concepts and capabilities
- Explain how AI systems learn from data, including supervised, unsupervised, and reinforcement learning
- Describe how generative AI produces new text, images, and other content
- Understand the architectures and techniques that enable AI systems to interpret data and automate decisions
- Identify common AI frameworks, models, and tools used in practice
- Explain the role of data quality, scale, and bias in training AI models
- Describe the impact of training data on AI model performance and reliability
- Understand the operational aspects of deploying and managing AI systems in real-world settings
- Explain foundational concepts of neural networks and deep learning
- Describe natural language processing (NLP) and computer vision fundamentals
- Identify ethical considerations and responsible AI principles
- Understand the difference between narrow AI and general AI concepts
- Describe real-world applications of AI across industries
- Evaluate AI solutions for practical business and technical use cases
Benefits
- This is a flat-fee engagement, paid upon successful completion of the assessment and survey.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
artificial intelligencesupervised learningunsupervised learningreinforcement learninggenerative AIneural networksdeep learningnatural language processingcomputer visiondata quality