VP, Product Management

Uniphore is the Business AI company. Our sovereign, composable and secure AI platform connects enterprise data, fine-tunes AI models and deploys agentic AI across the enterprise. We empower every worker to boost productivity and help businesses grow faster, operate smarter and reduce costs. Trusted by more than 2,000 businesses globally, and recognized on the Deloitte Fast 500, Uniphore delivers on the promise of AI as a transformative force for business. Job Description: Job Description: VP, Product Management The Vice President of Data Products   is responsible for   overseeing the vision, strategy, and execution of the company’s data platforms. Our data platforms play multiple roles, including:    As internal capabilities,   they   power our GenAI stack (access, transform, and supply for GenAI model training data), our CDP product and our applications    As customer-facing products, they provide Zero-ETL access to Enterprise data and a rich suite of capabilities to connect, clean, orchestrate and   operationalize   data     Our Zero Data AI Cloud, positioned around the concept of being able to   leverage   Enterprise data more quickly and flexibly than any other approach, is anchored on this suite of data platforms that this role will inherit and expand.     This is the   GM of the Data Product business   and ensures the development and delivery of innovative data products and solutions that align with business   objectives , enhance decision- making   and create competitive advantages.   The   VP   leads cross-functional teams to design, build, and   maintain   scalable data products,   deliver on   revenue goals, and while fostering a data-driven culture.    Key Responsibilities     Strategic Leadership     Define and execute the strategic roadmap for data platforms, aligning with the company’s business goals, financial   outcomes,  and   customer needs.    Develop and execute   a data   readiness strategy to support internal   activities  aligned   with   thewhich   align with the Engineering and   AI  organization's   architecture approaches and   andsupport   Customer   discovery and insights from   their’s   AI goals.    Collaborate with executive leadership to   identify   and prioritize data innovation, AI use cases, and monetization that require high-quality, accessible, and reliable data products.    Build and communicate a vision for how data products can deliver business value and drive growth.    AI Data Readiness     Ensure that all data platforms   provide   robust   capabilities,   assets are for cleaning, annotated, structured, and   optimized   for AI and machine learning purposes.    Drive the adoption of modern data management practices, including data labeling, feature engineering, and real-time data streaming for AI within   Uniphore   and supporting sales during customer interactions.    Partner with data scientists and engineers to define requirements and   participate   with Engineering and Architecture teams in designing scalable AI pipelines and   MLOps   workflows.    Deep LLM & Generative AI Architecture   Deep understanding of Large Language Models (LLMs) and Generative AI architectures, including transformer-based models, embeddings, prompt/context engineering, model selection, fine-tuning, RAG, and the tradeoffs between proprietary and open-source models.   Agentic AI & AI Application Architecture   Expertise   in emerging agentic AI architectures, including AI agents, tool/function calling, orchestration, memory, multi-agent systems, workflow automation, and human-in-the-loop patterns, with the ability to translate these capabilities into scalable enterprise products.   AI Evaluation, Reliability & Production Optimization   Deep understanding of evaluating and operationalizing AI/LLM systems in production, including model and application evaluation, quality measurement, latency, cost/performance optimization, observability, model drift, feedback loops, and continuous improvement.   AI Safety, Security & Responsible AI   Strong understanding of enterprise AI safety, security, and responsible AI practices, including AI governance, model risk, prompt injection, data leakage, bias, privacy, guardrails, access controls, explainability, and human oversight.   Product Management for Data Platforms     ​ Lead the high-level design and collaborate with AI Engineering and Data Engineering Leadership, define development, and deployment of scalable requirements, user-centric data   products   and platforms.    Prioritize product features and enhancements based on customer feedback, market trends, and business needs for monetization  Ensure   the use of best practices in data science, analytics, and engineering in product design.    Oversee the creation and deployment of data products, including training datasets, feature stores, and synthetic data generation tools, that enable AI innovation.    GTM experience working with Sales, performing competitor analysis, pricing, experience working with Product Marketing and partner teams on expanding product growth and delivering on revenue targets    Ability to define   a product roadmap, prioritize feature, functionality, and capabilities that are needed by customers, based on the competitive landscape, analyst reports, etc. ​   Team Leadership     Build, mentor, and lead a multidisciplinary team of product managers, data engineers, and AI specialists.    Foster collaboration across engineering, design, sales, marketing, and other departments.    Promote a culture of continuous learning, experimentation, and data-driven decision-making.    Create a collaborative culture that bridges data engineering, product development, and AI research teams.    Develop skill-building programs to keep the team   up-to-date   with the latest AI and data trends.    ​   Technology & Infrastructure     Partner with engineering teams to e