Description

Responsibilities

• Research emerging trends, technologies, industry standards, and best practices across 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 to identify opportunities for new or improved capabilities.

• Translate technology developments into actionable 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 in partnership with engineering and architecture teams.

• Serve as the bridge between business, engineering, data science, architecture, and AI/ML teams,

translating complex technical concepts into clear product decisions.

• Evaluate build-vs-buy-vs-partner opportunities and articulate associated technical and product

trade-offs.

• Develop structured evaluation frameworks and benchmarks for comparing AI models, platforms,

and Document Intelligence solutions.

• Design and execute experiments measuring factors such as 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 closely with engineers and data scientists to design and execute PoCs, prototypes, and

technical experiments for emerging AI technologies.

• Define PoC hypotheses, datasets, evaluation criteria, success metrics, and recommendations for

production adoption.

• Identify technical risks, limitations, dependencies, and scaling considerations before solutions

progress toward production.

• Produce high-quality technical assessments, benchmarking reports, architecture/product

recommendations, decision documents, and technology landscape analyses.

• Synthesize complex research and experimental results into clear recommendations for both

technical teams and senior leadership.

• Maintain reusable documentation covering evaluated technologies, benchmarks, lessons learned,

and technology decisions.

• Develop executive-ready materials that clearly communicate opportunities, trade-offs, risks,

investment considerations, and recommended next steps.

Qualifications

• 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-vs-buy alternatives.

• Familiarity with AI evaluation methodologies, including ground-truth dataset creation, accuracy

metrics, model evaluation, latency/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

Key Skills

Education

Bachelor's degree