Description
Key Skills: Artificial Intelligence, Machine Learning, System Architecture, Large Language Models, RAG, API Integration, Python, Knowledge Management, Technical Documentation, Governance
Good to Have Skills: Experience with agentic AI platforms, Model Context Protocol (MCP), tool calling mechanisms, connector protocols, retrieval frameworks, metadata management principles, version control systems, security standards implementation, proof-of-concept development, and cross-functional collaboration in product planning.
Roles & Responsibilities:
- Develop and maintain deep expertise in agentic entity sets including tool calls, connectors, APIs, and native integrations.
- Build comprehensive command of system instructions and skills architecture for reliable end-to-end workflows.
- Architect retrieval frameworks that ground AI outputs in verified, properly attributed organizational data.
- Own and maintain the canonical AI skill catalogue as quality-assured library of tools and integrations.
- Apply rigorous strategic value tests to ensure skills encode proprietary organizational workflows and data sources.
- Set contribution standards, version control, and deprecation policies for AI skill management.
- Drive cultural change needed to embed AI capability across all organizational functions effectively.
- Identify, sponsor, and develop AI champions across the business for long-term culture building.
- Deliver proof-of-concept reference implementations demonstrating best practices in design and documentation.
- Serve as expert voice on AI knowledge quality in product planning and leadership forums.
- Partner with Product, AI Engineering, and Research teams to support commercial and product ambitions.
Experience Required: Deep practical understanding of agentic AI platforms and large language models, hands-on experience building AI skills and workflows, experience curating shared tool libraries or platform standards, and familiarity with knowledge and metadata management principles