An overview of GenAI skills
A guide to the diverse work areas within Gen-AI to help recruiters identify and attract the right talent.
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by Nikunj Verma
Gen-AI Work Areas
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Research and Development
Exploring novel applications, prototyping new features
Prompt Engineering
Designing and optimizing prompts for various AI tasks
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AIMLOps
Managing the operations and infrastructure specific to Gen-AI systems
GenAI Frontend Development
Building user interfaces for AI-powered applications
GenAI Backend Development
Powering the AI engine
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GenAI - QA
Ensuring rigorous quality and safety standards through thorough testing and validation.
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AI Ethics and Governance
Implementing responsible AI practices, maintaining compliance, and addressing societal implications.
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Advanced AI Systems
Creating AI libraries, tools, frameworks to enable scalable and reliable AI development.
1. Research and Development: Pushing the Boundaries
Exploring Novel Applications
Gen-AI researchers explore uncharted territories, experimenting with new techniques and models to solve complex problems. They work with domain experts to identify unique use cases and develop innovative solutions.
Prototyping New Features
They create and test new AI-powered features, working closely with product teams to translate ideas into user-friendly applications.
Advancing the State of the Art
Gen-AI researchers conduct cutting-edge research and publish groundbreaking papers, driving the field forward and shaping the future of AI.
2. Prompt Engineering: Crafting the Optimal Input
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Prompt Design
Prompt engineers craft prompts that elicit desired responses from large language models.
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Prompt Optimization
They refine prompts through iterative testing to improve the accuracy and coherence of AI outputs.
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Prompt Incorporation
Prompt engineers collaborate with developers to integrate prompts into AI-powered applications.
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Prompt Documentation
Prompt engineers document prompts, their use cases, and performance metrics.
3. AI Frontend Development: Bringing AI to Users
UI Design
Frontend developers design user interfaces that showcase AI applications, collaborating with UX designers for an engaging experience.
Interactive Experiences
They build dynamic experiences that allow users to interact directly with AI models, using various methods.
Seamless Integration
Frontend developers ensure a smooth integration with backend AI systems, creating a cohesive and responsive user experience.
4. Backend Development: Harnessing the AI Models
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API Integration
Backend developers integrate AI models with APIs for seamless data exchange and system integration.
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Model Deployment
They deploy and manage scalable, reliable AI models, often using containerization and orchestration.
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Inference Optimization
These developers optimize the inference process for efficient and fast AI responses.
5. AIMLOps: Ensuring Seamless AI Workflows
Model Versioning
AIMLOps professionals manage model versions for traceability, reproducibility, and rollback.
Automated Workflows
They automate the entire AI lifecycle, from data to deployment.
Monitoring and Alerting
AIMLOps implement systems to track model health and performance.
Scaling Infrastructure
They ensure infrastructure scales for growing AI demands.
6. Quality Assurance and Testing: Ensuring Reliability and Safety
Bias and Fairness Testing
QA professionals test Gen-AI models for bias, ensuring ethical and non-discriminatory decisions.
Performance Evaluation
They measure accuracy, reliability, and consistency of AI outputs using benchmarks and test cases.
Safety Compliance
Gen-AI specialists ensure applications adhere to safety standards and regulations to protect users and mitigate risks.
Integration Testing
QA professionals rigorously test the integration of AI models, verifying seamless data flow and error-free interactions.
7. AI Ethics & Governance
- AI Bias Identification - Analyze AI models for unfair biases and discrimination, ensuring equitable decision-making.
- Algorithmic Transparency - Develop frameworks to increase the interpretability and explainability of AI systems.
- Ethical Frameworks - Establish ethical guidelines and governance policies to align AI development with societal values.
- Compliance Monitoring - Ensure AI-powered applications adhere to relevant regulations and privacy laws.
- Responsible AI Auditing - Audit AI systems to evaluate their safety, security, and adherence to ethical principles.
8. Advanced AI Systems
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AI Frameworks
Foundational software structures that enable the development of powerful AI applications
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AI Libraries
Curated collections of pre-built AI algorithms and models for plug-and-play functionality
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AI Agents
Autonomous AI-powered entities that can interact with and make decisions in complex environments
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AI Tools
Specialized software applications that assist in the development, deployment, and management of AI systems
The "Advanced AI Systems" work area focuses on creating powerful frameworks, comprehensive libraries, intelligent agents, and specialized tools to help push the boundaries of Artificial General Intelligence (AGI).
Recap of key GenAI work areas
- Research & Development: Pushing the boundaries of AI capabilities.
- Prompt Engineering: Crafting optimal inputs to elicit desired AI outputs.
- AI Frontend Development: Integrating AI into user-facing applications.
- Backend Development: Harnessing the power of AI models in production.
- AIMLOps: Ensuring seamless workflows for AI model deployment and maintenance.
- Quality Assurance: Validating the reliability and safety of AI systems.
- AI Ethics & Governance: Aligning AI development with societal values.
- Advanced AI Systems: Creating foundational building blocks for the future of AI.
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