The Director of AI Strategy and Architecture will be responsible for leading the development and deployment of AI solutions across the enterprise, with a particular focus on Retrieval Augmented Generation (RAG) and integration with enterprise data governance frameworks. This role is crucial in driving AI innovation, establishing enterprise standards for AI tools, and ensuring the successful delivery of AI and machine learning (ML) solutions that align with business objectives.
About the Role:
Define and execute the AI strategy and roadmap, ensuring alignment with enterprise goals and data governance frameworks.
Responsibilities:
AI Strategy and Roadmap: Define and execute the AI strategy and roadmap, ensuring alignment with enterprise goals and data governance frameworks.
RAG Solutions: Develop and implement scalable RAG systems, integrating external data sources with AI models to enhance information retrieval and knowledge generation capabilities.
AI Architecture: Design and oversee the implementation of AI solutions leveraging state-of-the-art architectures such as transformers, diffusion models, and RAG systems, ensuring compliance with enterprise data governance policies.
Data Governance Integration: Collaborate with the corporate data governance team to integrate AI solutions with the company's enterprise data governance framework, focusing on data quality, security, and compliance.
AI Governance: Establish and maintain enterprise standards for AI architecture, tools, and governance frameworks, ensuring alignment with overall enterprise data governance policies.
Team Leadership: Lead and mentor high-performing AI teams, providing strategic direction in the development and deployment of AI solutions.
Model Development and Fine-Tuning: Guide the design, development, and deployment of both commercial and open-source AI models, including transformers, generative models, large language models (LLMs), and RAG implementations. Ensure models are tailored to specific enterprise needs and integrated seamlessly into AI solutions.
Data Science and ML Ops: Oversee the implementation of data science and ML operations practices to ensure efficient and scalable AI development and deployment. This includes managing data pipelines, model training and evaluation, and continuous integration and delivery (CI/CD) processes.
LLM Ops: Lead the development and management of operations for large language models (LLMs), including model selection, fine-tuning, and deployment. Ensure that LLM operations align with enterprise data governance policies and support the delivery of high-quality AI solutions.
Collaboration: Partner with IT, business units, and other stakeholders to ensure AI initiatives are aligned with business needs and integrated into the broader enterprise architecture and data governance framework.
Database Management: Work with SQL and NoSQL databases to ensure AI models and RAG systems can efficiently access and utilize enterprise data, while adhering to data governance standards.
Cloud Integration: Leverage cloud platforms like Azure for AI model deployment, data management, and large-scale AI operations.
Emerging Technologies: Stay updated on the latest AI trends and tools, such as Langchain, LlamaIndex, and other ecosystem tools critical for AI advancements, with a focus on Retrieval Augmented Generation and data governance compliance.
Qualifications:
15+ years of experience in data, AI, and ML, with a proven track record of delivering enterprise-level AI solutions.
Advanced understanding of Generative AI architectures, including transformers and diffusion models, and experience in deploying large-scale models.
Experience with Retrieval Augmented Generation (RAG) systems, including integrating external knowledge bases with AI models.
Expertise in Langchain and LlamaIndex.
Experience with SQL and NoSQL databases and their integration with AI models and RAG systems.
Familiarity with enterprise data governance frameworks and the ability to integrate AI solutions within these frameworks.
Familiarity with Azure cloud services and other cloud-based AI deployment platforms.
Experience with data science and ML operations practices, including data pipelines, model training and evaluation, and CI/CD processes.
Experience with LLM operations, including model selection, fine-tuning, and deployment.
Experience with enterprise architecture frameworks (e.g., TOGAF, Zachman) for capturing, maintaining, and aligning AI solution architectures with broader enterprise architecture standards and business objectives.
Strong business acumen with the ability to translate technical AI concepts into business value.
Master's degree in Data Science, AI, Computer Science, or a related field (preferred).
Preferred Skills:
Strong leadership experience with the ability to lead cross-functional teams and drive enterprise-wide AI transformation.
Hands-on experience in implementing AI/ML and RAG pipelines in large organizations.
Familiarity with AI governance and data governance frameworks and best practices.
Effective communication skills for engaging with both technical teams and business stakeholders.