Senior AI Product Manager – Document Intelligence

VSG Business Solutions•Sunrise, NY

About The Position

The Senior AI Product Manager will drive product strategy, research, evaluation, and direction for AI-powered Document Intelligence capabilities. The role combines technical product management, market and technology research, and hands-on evaluation of AI solutions. The person in this role will assess emerging technologies and translate findings into product and technology recommendations, requirements, roadmaps, and investment opportunities. They will collaborate across business, engineering, data science, architecture, and AI/ML teams, and partner with engineers and data scientists on proofs of concept, prototypes, and experiments. The role also produces technical assessments, benchmarking reports, decision documents, reusable technology documentation, and executive-ready recommendations. The position calls for significant technical or AI product management experience, ideally in Document Intelligence, and strong knowledge of Agentic AI, Generative AI, LLMs, multimodal models, OCR, information extraction, and retrieval-augmented generation. The posting ends mid-qualification after requesting excellent analytical and written communication skills and the ability to create technical documentation, research reports, product requirements, and executive-level materials.

Requirements

Significant experience in technical or AI product management, ideally involving Document Intelligence.
Strong knowledge of Agentic AI, Generative AI, LLMs, multimodal models, OCR, information extraction, and RAG/retrieval.
Understanding of tool calling, planning, reasoning, orchestration, memory, context management, and human-in-the-loop patterns.
Experience defining technical products and requirements with Business, Engineering, Architecture, Security, and Data Science teams.
Demonstrated ability to use experimentation and quantitative evaluation to inform product decisions.
Experience assessing vendors, emerging technologies, and build-versus-buy alternatives.
Familiarity with AI evaluation methodologies, including ground-truth dataset creation, accuracy metrics, model evaluation, latency and cost analysis, and quality measurement.
Understanding of enterprise security, privacy, responsible AI, governance, scalability, and observability.
Excellent analytical and written communication skills, with demonstrated ability to create technical documentation, research reports, product requirements, and executive-level materials; the posting's final qualification is incomplete.

Responsibilities

Research trends, technologies, industry standards, and best practices in Document Intelligence, Agentic AI, Generative AI, multimodal AI, OCR, document understanding, extraction, classification, retrieval, and authentication.
Evaluate third-party tools, open-source technologies, foundation models, and cloud-native AI services for opportunities to add or improve capabilities.
Translate technology developments into product strategies, investment recommendations, and roadmap opportunities.
Own technical product strategy, requirements, and roadmap development for Document Intelligence capabilities.
Define product requirements, technical capabilities, APIs, integration patterns, success metrics, and non-functional requirements with engineering and architecture teams.
Bridge business, engineering, data science, architecture, and AI/ML teams, translating technical concepts into product decisions.
Evaluate build-versus-buy-versus-partner opportunities and explain technical and product trade-offs.
Develop evaluation frameworks and benchmarks for AI models, platforms, and Document Intelligence solutions.
Design and execute experiments measuring accuracy, extraction quality, latency, scalability, reliability, cost, integration complexity, and model performance.
Establish repeatable benchmarking methodologies and maintain a fact-based view of the competitive and technology landscape.
Partner with engineers and data scientists to design and execute proofs of concept, prototypes, and technical experiments.
Define proof-of-concept hypotheses, datasets, evaluation criteria, success metrics, and production-adoption recommendations.
Identify technical risks, limitations, dependencies, and scaling considerations before solutions move toward production.
Produce technical assessments, benchmarking reports, architecture and product recommendations, decision documents, and technology landscape analyses.
Synthesize research and experimental results into recommendations for technical teams and senior leadership.
Maintain reusable documentation covering evaluated technologies, benchmarks, lessons learned, and technology decisions.
Develop executive-ready materials communicating opportunities, trade-offs, risks, investment considerations, and recommended next steps.