Technical Product Lead

HIGH BRIDGE CONSULTING LLC

3d•$50 - $70•Remote

About The Position

Our client, a Fortune 500 financial and payments technology company, is seeking a Technical Product / Program Leader for a 100% remote role. The position will build the next generation of commercial knowledge capability, transforming a traditional knowledge repository into a trusted, measurable, scalable, AI-enabled knowledge ecosystem. It leads technical Knowledge Management and content/governance resources at the intersection of Knowledge Management, Generative AI, automation, information architecture, and technical product leadership. The role sets strategy and translates business needs into technical solutions, taking capabilities from concept through prototype, deployment, and measurable adoption. Over the first 12 months, the leader will establish measures of knowledge health and reliability, automate governance and content review, and create actionable reporting. They will also define the architecture and roadmap for agentic knowledge capabilities, lead or support AI-agent development and prototyping, and establish standards for retrieval, grounding, integration, security, and evaluation. The position will create a repeatable enterprise knowledge framework, including onboarding and adherence models for knowledge providers and standards for content review, metadata, taxonomy, ownership, lifecycle, and governance. The framework should enable teams to maintain trusted knowledge sustainably and make it available through search, enterprise workflows, and AI or agentic experiences. The leader works closely with engineers and architects and guides complex cross-functional technology programs.

Requirements

Demonstrated experience taking enterprise AI, Generative AI, or agentic solutions from concept through production and adoption.
Strong working knowledge of LLMs, Retrieval-Augmented Generation (RAG), enterprise or semantic search, and agentic architectures.
Experience designing or directing agent workflows, retrieval, and grounding approaches, including how AI accesses trusted enterprise information.
Experience integrating enterprise knowledge and data through APIs, connectors, retrieval pipelines, or similar approaches.
Strong understanding of knowledge architecture, taxonomy, metadata, content lifecycle, and information governance.
Experience measuring content quality, retrieval effectiveness, AI reliability, and/or user adoption.
Technical depth to work directly with engineers and architects, challenge technical decisions, and translate business requirements into scalable solutions.
Demonstrated ability to lead complex cross-functional technology programs and technical resources.
Experience across a combination of AI and retrieval technologies, including LLMs, RAG, agentic AI, multi-agent orchestration, semantic search, embeddings, vector search, AI grounding, and evaluation.
Experience across a combination of integration and data technologies, including APIs, JSON, enterprise connectors, structured and unstructured data, authentication and permissions, and cloud architecture.
Experience with some or all of the Microsoft ecosystem, including Microsoft 365, SharePoint, Copilot Studio, Power Platform, Power Automate, Microsoft Graph, and/or Azure AI.
Working knowledge of Python, SQL, low-code development, APIs, and modern AI prototyping techniques.
Ability to guide how solutions are built without needing to be the team's primary software engineer.

Nice To Haves

Ability to demonstrate a solution personally taken from problem definition through architecture, prototype, production, adoption, and measurement.
Experience grounding AI against trusted enterprise information, addressing governance and security, and measuring delivered value.

Responsibilities

Define KPIs for content accuracy, freshness, ownership, review adherence, reliability, and AI readiness.
Establish a baseline for trusted commercial knowledge and identify where intervention is required.
Measure and advance content-review and governance automation to reduce manual effort for content owners.
Create reporting that identifies healthy, stale, unowned, or unreliable content and supports continuous improvement.
Define the architecture and roadmap for agentic knowledge capabilities.
Lead or directly support the development and prototyping of AI agents grounded in enterprise knowledge.
Design how multiple agents and AI capabilities work together.
Establish retrieval, grounding, integration, security, and evaluation standards for production agentic capabilities.
Translate emerging AI capabilities into practical commercial use cases with technical resources.
Define onboarding and adherence models for internal knowledge providers.
Establish standards for content review, metadata, taxonomy, ownership, lifecycle, and governance.
Create a scalable model for teams to maintain their own knowledge while connecting governance and reporting to the central framework.
Enable trusted knowledge to be consumed through search, enterprise workflows, and AI or agentic experiences.
Lead complex cross-functional technology programs and technical resources.
Work alongside engineers and architects to guide solution architecture and translate business requirements into scalable technical solutions.