Model DevelopmentDevelop and implement generative AI models for various applications (text, image, audio, video). Research & Application: Research and apply cutting-edge techniques in deep learning, NLP, and computer vision. Data Pipelines: Design and build robust data pipelines for training and evaluating large-scale generative models.Performance Optimization: Optimize model performance, including reducing latency and improving accuracy. Cross-Functional Collaboration: Collaborate with cross-functional teams (product, engineering, research) to define and deliver AI-powered solutions. Architecture Selection: Evaluate and select appropriate generative AI architectures and frameworks. Model Fine-Tuning: Fine-tune pre-trained models for specific use cases and domains. Documentation: Develop and maintain documentation for models, data pipelines, and deployment processes. Production Monitoring: Monitor model performance in production and implement necessary updates and improvements. Staying Current: Stay abreast of the latest advancements in generative AI and related fields.Ethical Considerations: Address ethical considerations and biases in generative AI models.Security Implementation: Implement security measures to protect sensitive data and models. Prototyping & Experimentation: Prototype and experiment with new generative AI concepts and applications.Tool Development: Contribute to the development of internal tools and libraries for generative AI. Troubleshooting: Troubleshoot and resolve technical issues related to generative AI models and infrastructure.Technical Skills: Programming: Proficiency in Python. Deep Learning Frameworks: Expertise in TensorFlow and/or PyTorch. Generative Models: Deep understanding of GANs, VAEs, and transformer models. NLP: Strong knowledge of natural language processing techniques.Computer Vision: Familiarity with computer vision concepts and libraries. Cloud Computing: Experience with cloud platforms (AWS, Google Cloud, Azure) for AI deployment. Data Processing: Experience with data preprocessing and feature engineering
Model Development Develop and implement generative AI models for various applications (text, image, audio, video). Research & Application: Research and apply cutting-edge techniques in deep learning, NLP, and computer vision. Data Pipelines: Design and build robust data pipelines for training and evaluating large-scale generative models. Performance Optimization: Optimize model performance, including reducing latency and improving accuracy. Cross-Functional Collaboration: Collaborate with cross-functional teams (product, engineering, research) to define and deliver AI-powered solutions. Architecture Selection: Evaluate and select appropriate generative AI architectures and frameworks. Model Fine-Tuning: Fine-tune pre-trained models for specific use cases and domains. Documentation: Develop and maintain documentation for models, data pipelines, and deployment processes. Production Monitoring: Monitor model performance in production and implement necessary updates and improvements. Staying Current: Stay abreast of the latest advancements in generative AI and related fields. Ethical Considerations: Address ethical considerations and biases in generative AI models. Security Implementation: Implement security measures to protect sensitive data and models. Prototyping & Experimentation: Prototype and experiment with new generative AI concepts and applications. Tool Development: Contribute to the development of internal tools and libraries for generative AI. Troubleshooting: Troubleshoot and resolve technical issues related to generative AI models and infrastructure. Technical Skills: Programming: Proficiency in Python. Deep Learning Frameworks: Expertise in TensorFlow and/or PyTorch. Generative Models: Deep understanding of GANs, VAEs, and transformer models. NLP: Strong knowledge of natural language processing techniques. Computer Vision: Familiarity with computer vision concepts and libraries. Cloud Computing: Experience with cloud platforms (AWS, Google Cloud, Azure) for AI deployment. Data Processing: Experience with data preprocessing and feature engineering
