[Hiring] Technical Product Lead @HIGH BRIDGE CONSULTING LLC

Role Description

We are looking for a Technical Product / Program Leader to build the next generation of our commercial knowledge capability, moving us from a traditional knowledge repository to a trusted, measurable, scalable and AI-enabled knowledge ecosystem. This role will lead both technical Knowledge Management and content/governance resources and will work at the intersection of Knowledge Management, Generative AI, automation, information architecture and technical product leadership.

This is not a traditional Knowledge Management maintenance role. We need a leader who can set the strategy and get it built, translating business needs into technical solutions and moving capabilities from concept through prototype, deployment and measurable adoption.

What You Will Deliver in 12 Months

• Make our knowledge measurable and trustworthy
  • Establish how we measure the health and reliability of commercial knowledge and use automation to improve it.
  • Define KPIs for content accuracy, freshness, ownership, review adherence, reliability and AI readiness.
  • Establish a baseline showing how much of our current knowledge can be trusted and where intervention is required.
  • Measure and advance content review and governance automation, reducing manual effort for content owners.
  • Create actionable reporting that identifies healthy, stale, unowned or unreliable content and drives continuous improvement.
• Build our agentic knowledge capability
  • Transform the Knowledge Hub from a repository into an AI-enabled experience where intelligent agents can retrieve, connect and deliver trusted enterprise knowledge.
  • Define the architecture and roadmap for agentic 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, rather than creating disconnected AI use cases.
  • Establish the retrieval, grounding, integration, security and evaluation standards required to move agentic capabilities into production.
  • Work directly with technical resources to translate emerging AI capabilities into practical commercial use cases.
• Create a scalable enterprise knowledge framework
  • Turn what we build for Product Commercialization into a repeatable framework that other internal organizations can use to contribute trusted knowledge.
  • Define the onboarding and adherence model for internal knowledge providers.
  • Establish standards for content review, metadata, taxonomy, ownership, lifecycle and governance.
  • Create a scalable model that allows teams to maintain their own knowledge without creating unsustainable manual work for the central KM team.
  • Ensure trusted knowledge can ultimately be consumed through search, enterprise workflows and AI/agentic experiences.

Qualifications

• 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/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 integration 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.
• Sufficient 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.

Requirements

• Experience across a combination of AI & Retrieval: LLMs, RAG, agentic AI, multi-agent orchestration, semantic search, embeddings, vector search, AI grounding and evaluation.
• Integration & Data: APIs, JSON, enterprise connectors, structured/unstructured data, authentication/permissions and cloud architecture.
• Microsoft Ecosystem: Microsoft 365, SharePoint, Copilot Studio, Power Platform, Power Automate, Microsoft Graph and/or Azure AI.
• Prototyping: Working knowledge of Python, SQL, low-code development, APIs and modern AI prototyping techniques.
• You do not need to be the team's primary software engineer. You do need enough technical depth to sit alongside engineers and architects, understand the architecture, challenge it when necessary and actively guide how a solution gets built.

What Will Differentiate You

The strongest candidates will be able to demonstrate something they have personally taken through:

• Problem → Architecture → Prototype → Production → Adoption → Measurement

We want to understand what you built or directed, how you grounded AI against trusted enterprise information, how you addressed governance and security, and how you measured whether the solution delivered value. We are not looking for someone to simply administer today's Knowledge Management environment. We are looking for someone capable of defining and building what comes next.

This is a remote position.