GenAI-enabled Job Descriptions & skills
GenAI Skills Back-end Engineers Need Today
- Python Proficiency: Understand Python and neural networks.
- AI Integration: Leverage Generative AI in development.
- Automation: Use AI for task automation.
- Code Generation: Employ AI for coding.
- Debugging: Enhance debugging with AI.
- Testing: Improve testing via AI.
- Innovation: Create novel AI-driven applications.
- Feature Integration: Integrate AI features into tech stacks.
- Architecture Knowledge: Understand AI architectures and types.
- Project Planning: Stay relevant in AI project planning.
GenAI Skills Front-end Engineers Need Today
- Python Basics: Understand Python and neural networks.
- AI Integration: Utilize Generative AI in development.
- Automation: Automate tasks with AI.
- Code Generation: Leverage AI for coding.
- Debugging: Enhance debugging processes.
- Testing: Improve testing with AI.
- UI/UX Innovation: Innovate user interfaces using AI.
- Feature Integration: Integrate AI features into front-end projects.
- Performance Optimization: Use AI to optimize performance.
- Architecture Knowledge: Understand AI architectures and capabilities.
GenAI Skills Full-stack Engineers Need Today
- Python Basics: Understand Python and neural networks.
- AI Integration: Leverage Generative AI in development.
- Automation: Automate tasks with AI.
- Code Generation: Use AI for coding.
- Debugging: Enhance debugging processes.
- Testing: Improve testing efficiency.
- Innovative Applications: Create novel AI-driven applications.
- Feature Integration: Integrate AI features into tech stacks.
- Performance Optimization: Optimize performance using AI.
- Architecture Knowledge: Understand AI architectures and capabilities.
GenAI Skills Test Engineers Need Today
- LLM Implementation: Test applications using LLMs.
- Neural Network Knowledge: Understand various neural architectures.
- Model Behavior: Identify model failure points.
- Language Models: Grasp intricacies of language and generative models.
- Transformers: Understand transformer mechanisms.
- Scenario Creation: Generate diverse test scenarios with AI.
- Regression Testing: Enhance regression testing efficiency.
- Focus on Complex Tasks: Prioritize tasks requiring human intuition.
- Model Size Knowledge: Understand model size and training data implications.
- Scalability Monitoring: Test scalability and efficiency of AI models.
GenAI Skills Product Managers Need Today
- AI Knowledge: Understand generative AI basics and limitations.
- User Experience: Enhance experiences through automated content and personalized interactions.
- Strategic Use: Deploy AI for competitive advantages and market opportunities.
- Integration: Integrate AI in product development.
- Communication: Collaborate effectively with technical teams.
- Impact: Evaluate AI's effect on user interfaces.
- Ethics: Ensure ethical AI use and compliance.
GenAI Skills Engineering Managers Need Today
- Feasibility Assessment: Assess technical feasibility and ROI of GenAI projects.
- Market Trends: Understand trends and competitive advantages.
- Project Prioritization: Prioritize projects aligned with business goals.
- Team Management: Break down AI features and assign tasks.
- Challenge Understanding: Grasp AI implementation challenges and timelines.
- Innovation Potential: Recognize GenAI's potential for problem-solving.
- Project Lifecycle: Manage AI projects from data collection to deployment.
- Technical Guidance: Provide technical guidance on GenAI projects.
- Experimentation: Explore and run small-scale AI experiments.
- Evaluation Skills: Evaluate LLMs and market offerings for build-vs-buy decisions.
GenAI Skills Technical Program Managers Need Today
- Risk Management: Simulate and prepare for project risks with GenAI.
- Risk Assessment: Use LLMs to assess project health and identify risk patterns.
- Technical Understanding: Grasp GenAI fundamentals and technical challenges.
- AI Planning: Plan AI models, timelines, and associated risks.
- Experimentation: Conduct and convert AI experiments into production solutions.
- Automation: Automate project scheduling, resource allocation, and task prioritization.
- Routine Monitoring: Automate monitoring and reporting tasks.
- Strategic Focus: Free up time for strategic planning.
- Insight Generation: Generate GenAI-enabled insights for project adjustments.
- Enhanced Accuracy: Improve analysis accuracy with AI.
GenAI Skills ML Engineers Need Today
- Advanced Model Development: Create innovative AI models.
- Problem Solving: Solve complex problems with GenAI.
- State-of-the-Art Projects: Work on cutting-edge AI technologies.
- Job Market Competitiveness: Enhance employability with advanced skills.
- Efficiency: Improve workflows via automation and synthetic data generation.
- Model Performance: Enhance models with fine-tuning and transfer learning.
- AI-Driven Solutions: Develop AI that understands/generates text, images, audio.
- Stay Current: Keep skills updated with latest GenAI advancements.
- Resource Optimization: Reduce computational costs with AI optimizations.
- Security and Ethics: Mitigate biases and vulnerabilities in models.
GenAI Skills Data Scientists Need Today
- Model Innovation: Develop advanced AI models.
- Automation: Automate data preprocessing and feature engineering.
- Data Augmentation: Generate synthetic data for training.
- Model Improvement: Enhance models with fine-tuning and transfer learning.
- AI Solutions: Create AI-driven applications (chatbots, recommendation systems).
- Productivity: Boost efficiency with AI-driven workflows.
- Optimization: Optimize model performance and scalability.
- Ethics and Security: Mitigate biases and vulnerabilities.
- Decision-Making: Improve decisions with AI-driven insights.
- Competitive Edge: Stay current with AI advancements for market competitiveness.
