Job Description:
The Senior Generative AI Architect will be responsible for designing, developing, and implementing generative AI solutions that align with the company's strategic objectives. This role involves leading the architecture and deployment of advanced AI models, ensuring scalability, security, and ethical considerations are integrated into all AI initiatives. The ideal candidate will possess deep expertise in generative AI technologies, a strong understanding of AI ethics, and the ability to collaborate effectively with cross-functional teams.
Key Responsibilities:
• Architecture Design: Develop and maintain the architectural framework for generative AI solutions, ensuring alignment with business goals and technical standards.
• Model Development: Lead the design, training, and deployment of generative AI models (e.g., GPT, DALL-E, Stable Diffusion) tailored to various applications such as content generation, data synthesis, and automation.
• Integration: Collaborate with software engineering teams to integrate generative AI capabilities into existing systems and workflows.
• Scalability & Performance: Ensure AI solutions are scalable, efficient, and optimized for performance across different platforms and environments.
• Ethical AI Practices: Implement and enforce ethical guidelines for AI development and deployment, addressing issues such as bias, fairness, and transparency.
• Research & Innovation: Stay abreast of the latest advancements in generative AI and related fields, incorporating new techniques and tools into the company's AI strategy.
• Collaboration: Work closely with data scientists, engineers, product managers, and other stakeholders to identify opportunities for AI-driven solutions and ensure successful project delivery.
• Documentation & Standards: Create comprehensive documentation for AI architectures, processes, and best practices.
• Establish and maintain coding and architectural standards.
Mandatory
• Neutral network experience , RAG , Lang tree, and Lang graph experience on IT projects not on dummy project / Research projects.
• Consuming agents/graphs/vectors and how to use those
• Vector database and cosine similarity
• Neural network
• Finetuning RAG