Generative AI Architect

We are seeking an experienced and forward-thinking Generative AI Architect with a background of 12-15 years in AI/ML, with a specific focus on working with LLMs (Large Language Models) and Generative AI solutions. This pivotal role involves designing and supervising the development of cutting-edge GenAI platforms and solutions that revolutionize business operations and enhance customer experiences. As the GenAI Architect, you will collaborate closely with Data Scientists, ML Engineers, Product Teams, and Stakeholders to envision, prototype, and scale Generative AI Use Cases throughout the organization or for clients. Key responsibilities include leading the design and implementation of scalable GenAI solutions utilizing LLMs, diffusion models, and multimodal architectures. You will also be tasked with architecting end-to-end pipelines that encompass prompt engineering, vector databases, retrieval-augmented generation (RAG), and LLM fine-tuning. Furthermore, you will play a crucial role in selecting and integrating foundational models based on business requirements and technical limitations, defining GenAI architecture blueprints and best practices, and guiding teams on model evaluation, inference optimization, and cost-effective scaling strategies. Additionally, staying abreast of the rapidly evolving GenAI landscape, evaluating emerging tools, APIs, and frameworks, and collaborating with various stakeholders to identify high-impact GenAI use cases are essential aspects of this role. Requirements: - A Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field. A PhD would be advantageous. - 12-15 years of experience in AI/ML and software engineering, with at least 3 years of dedicated focus on Generative AI and LLM-based architectures. Core Skills: - Profound expertise in machine learning, natural language processing (NLP), and deep learning architectures. - Hands-on familiarity with LLMs, transformers, fine-tuning techniques, and prompt engineering. - Proficiency in Python, including working knowledge of libraries/frameworks like Hugging Face Transformers, LangChain, OpenAI API, PyTorch, and TensorFlow. - Experience with vector databases and RAG pipelines. - Strong grasp of cloud-native AI architectures, containerization, and API integration. Architectural & Leadership Skills: - Demonstrated ability to design and implement scalable, secure, and efficient GenAI systems. - Excellent communication skills for effective cross-functional collaboration and stakeholder engagement. - Capacity to mentor engineering teams and foster innovation within the AI/ML ecosystem. Nice-to-Have: - Previous experience with multimodal models. - Understanding of AI governance, ethical AI, and compliance frameworks. - Familiarity with MLOps practices for GenAI, encompassing model versioning, drift detection, and performance monitoring.,