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 recommendations
