Our client in the financial technology industry is looking for a Technical Product / Program Manager focused on AI initiatives to join their team!
Location: Remote. Candidates must be located in the United States.
Pay: $80-$90/hour W2
This role is not open to third parties on C2C.
Here is what you will be doing:
- Set the strategic vision and lead technical execution to transition the commercial Knowledge Hub from a legacy repository into an intelligent agentic architecture.
- Define and monitor key metrics for content accuracy, freshness, ownership, review adherence, and overall AI readiness across the organization.
- Direct the prototyping, development, and integration of AI agents grounded in trusted enterprise information using RAG and multi-agent orchestration.
- Establish baseline content quality metrics and deploy automated governance workflows to streamline review processes for content owners.
- Design robust retrieval, grounding, security, authentication, and evaluation standards to move experimental AI capabilities into enterprise production.
- Build actionable reporting tools to continuously flag stale, unowned, or unreliable content before it impacts end users or AI systems.
- Architect a scalable, repeatable enterprise knowledge framework that other business units can easily adopt to onboard and maintain their data sources.
- Oversee content taxonomies, metadata structures, and lifecycle policies to ensure smooth data consumption across enterprise search and automated workflows.
- Partner directly with software engineers, system architects, and cross-functional teams to translate business needs into scalable technical solutions.
- Lead both technical Knowledge Management engineering resources and content/governance teams across multi-phase project execution.
Here is what our ideal candidate has:
- 7-10+ years of Product/Program Management experience.
- Proven track record leading enterprise Generative AI or agentic AI solutions from early concept and prototyping through production and widespread adoption.
- Deep technical understanding of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector search, embeddings, and semantic search.
- Hands-on experience designing agent workflows, retrieval mechanisms, and grounding techniques against enterprise data stores.
- Strong knowledge of integration architectures, including APIs, JSON, enterprise system connectors, unstructured data pipelines, and cloud environments.
- Proficiency within the Microsoft Ecosystem, including Microsoft 365, SharePoint, Copilot Studio, Power Platform, Power Automate, Microsoft Graph, or Azure AI.
- Exposure to modern prototyping technologies, such as Python, SQL, low-code platforms, and modern AI development frameworks.
- Experience quantifying content quality, retrieval accuracy, AI reliability, and user adoption metrics.
- Ability to demonstrate a personal end-to-end delivery lifecycle: Problem to Architecture to Prototype to Production to Adoption to Measurement.