Generative AI Architect

Position:

Generative AI Architect (Specializing in Embeddings and Vector Space Models)

We are seeking a highly skilled Generative AI Architect with expertise in Azure, OpenAI, Python programming, and a deep focus on embeddings and vector space models. The role involves developing state-of-the-art AI systems that leverage advanced techniques such as multimodal models, large-scale embeddings, and foundational models to understand, generate, and optimize complex data representations.

Key Responsibilities:

Generative AI Development: Lead the design and development of cutting-edge generative AI systems using Azure cloud services and OpenAI technologies. Focus on building efficient, scalable architectures that incorporate the latest techniques, such as transformer-based architectures and foundation models like GPT-4 and beyond.

Multimodal AI Integration: Work with multimodal AI systems that can process and generate across various data types (text, images, audio, and video), leveraging large-scale embeddings and cross-modal understanding. Apply advances in diffusion models, synthetic data generation, and foundation models.

Foundation Model Fine-Tuning: Apply extensive experience in fine-tuning large language models (LLMs) and foundation models, particularly those used in embeddings and vector space models. Adapt these models for specific applications, improving their efficiency and accuracy in understanding complex data structures.

Azure and OpenAI Integration: Manage and optimize the deployment of generative AI projects on Azure, ensuring robust integration with OpenAI's APIs and tools. Leverage cloud-native architectures to build AI solutions that scale.

Advanced Python and AI Framework Expertise: Master Python programming within the context of modern AI and machine learning libraries (e.g., PyTorch, TensorFlow, Hugging Face). Focus on developing and manipulating embeddings and vector space models using the latest advancements in machine learning and AI tools.

Embeddings and Vector Space Mastery: Demonstrate deep knowledge of embedding techniques and vector space models, especially in the context of generative AI. Implement state-of-the-art strategies for creating, optimizing, and utilizing embeddings for various data types (e.g., large-scale text, images, audio).

AI-Driven Personalization & Search: Lead initiatives that utilize embeddings and generative AI to develop personalized solutions, advanced search algorithms, and recommendations, leveraging AI for better data understanding and user interaction.

Responsible AI & Ethics: Uphold and advocate for the responsible use of AI technologies, including ethical AI development, data privacy, bias mitigation, and alignment with compliance standards such as HIPAA, GDPR, and other relevant regulations.

Model Optimization & Monitoring: Continuously optimize generative AI models, focusing on vector space efficiency, inference speed, and memory management in large-scale AI applications. Apply the latest techniques in model distillation, pruning, and efficient finetuning.

Thought Leadership & Mentorship: Stay at the forefront of generative AI developments, including advancements in embeddings, LLMs, and foundation models. Mentor team members and drive knowledge sharing across interdisciplinary teams.

Risk Management & Governance: Evaluate risks in generative AI initiatives, with a particular focus on embeddings and vector space models. Implement strategies to mitigate risks associated with AI model usage and performance, including ethical considerations in AI applications.

Qualifications:

Advanced degree in Computer Science, AI, Machine Learning, or a related field.

Extensive experience in developing generative AI systems, with particular expertise in embeddings, vector space models, and multimodal AI.

Proficiency in Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow, Hugging Face).

Demonstrated experience working with Azure and OpenAI platforms, including fine-tuning and deploying large models.

Strong analytical, leadership, and communication skills with a track record of successful collaboration across interdisciplinary teams.

Commitment to responsible AI practices, with knowledge of AI ethics and compliance standards (HIPAA, GDPR, etc.).