Senior AI Product Manager – Document Intelligence

OmegaHires; Phoenix (AZ); On-site; USD 140,000 - 190,000; Full time

Job summary

Omegahires is seeking an AI Product Manager to drive strategy, research, evaluation, and direction for AI-powered Document Intelligence capabilities. This role blends technical product management with market and technology assessment.

The ideal candidate has deep knowledge of Agentic AI, Generative AI, LLMs, multimodal models, OCR, extraction, and retrieval. You will evaluate solutions, design experiments, support proofs of concept, and translate findings into product recommendations.

Qualifications

• Significant experience in technical or AI product management for Document Intelligence.
• Strong knowledge of Agentic AI, Generative AI, LLMs, multimodal models, OCR, information extraction, and retrieval.
• Understanding 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.

Responsibilities

• Research trends and standards across Document Intelligence, Agentic AI, Generative AI, multimodal AI, OCR, 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.

Skills

Agentic AI

Generative AI

LLMs

Multimodal models

OCR

Information extraction

Retrieval systems

Experimentation

Executive communication

Job description

We are seeking an AI Product Manager to drive product strategy, research, evaluation, and direction for AI-powered Document Intelligence capabilities. This role combines technical product management,

market and technology research, and hands-on evaluation of AI solutions.

The ideal candidate will have a strong understanding of Agentic AI, Generative AI, LLMs, multimodal models, OCR, document parsing and extraction, retrieval technologies, with the technical depth to

evaluate solutions, design experiments, support Proofs of Concept, and translate findings into product and technology recommendations.

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.