About the Role
We are building the next-generation AI and data platform to power Agentic AI and Knowledge AI applications across the enterprise—applications that reason, retrieve, act, and adapt in real-time. As VP of Product Management, you will lead the charge in defining and delivering the foundational platform capabilities that make this possible: advanced Retrieval-Augmented Generation (RAG) pipelines, intelligent agent frameworks, scalable workflow and orchestration, and enterprise-grade AI governance.
This role is ideal for a platform product leader who understands both the technical underpinnings of enterprise AI and the ecosystem dynamics of operating alongside (and against) hyperscalers like AWS, Azure, Google Cloud, and strategic players like Databricks, Snowflake, Salesforce, and ServiceNow.
Platform Vision & Execution
- Own the end-to-end product strategy for our AI, supporting agent-based and retrieval-augmented use cases across enterprise functions (support, finance, HR, operations).
- Define and evolve core capabilities across:
- Advanced RAG frameworks (Document RAG, Conversation RAG, Domain-aware RAG).
- Agent Builder tools enabling reusable, composable AI agents with memory, planning, and reasoning.
- Workflow and orchestration through BPMN-based builders and event-driven runtime.
- Guardrails, explainability, and policy controls to ensure safe and governed AI execution.
- Lead roadmap execution in partnership with Engineering, Design, and AI Research teams.
Ecosystem Awareness & Differentiation
- Develop a sharp understanding of competitive positioning and ecosystem overlaps with:
- Hyperscalers’ AI offerings (Amazon Bedrock, Azure OpenAI, Google Vertex).
- Application-centric platforms (Salesforce Einstein, ServiceNow Now Platform).
- Identify areas to integrate, interoperate, or compete—striking the right balance of leverage vs. defensibility.
- Partner with alliances, go-to-market, and ecosystem teams to drive strategic integrations and positioning.
Customer-Led Innovation
- Engage directly with enterprise architects, CIOs, CDOs, and AI/ML teams to uncover platform needs, pain points, and innovation opportunities.
- Translate customer use cases into scalable platform features—especially across domains like customer service, finance automation, and enterprise knowledge management.
- Drive platform adoption through developer enablement, SDKs, APIs, and an ecosystem of reusable agents and workflows.
Org & Product Leadership
- Lead and scale a world-class PM organization across platform services, AI orchestration, governance, and integrations.
- Establish product processes for roadmapping, customer validation, experimentation, and portfolio planning.
- Partner closely with Engineering, Legal, Security, and GTM to drive trusted AI adoption at scale.
What You’ll Bring
Must-Have Experience
- 15+ years of experience in product management, with 5+ years leading platform teams at scale.
- Strong track record in enterprise AI, data platforms, or developer infrastructure.
- Deep familiarity with:
- RAG architectures, vector search, chunking, embeddings, and multi-hop retrieval.
- Agent frameworks (planning, memory, tools, persona modeling).
- AI Governance – including policy engines, auditability, model monitoring, and compliance alignment.
- BPMN, workflow engines, and orchestration systems in enterprise IT.
Strategic & Execution Skills
- Visionary mindset coupled with operational rigor.
- Comfort navigating platform-vs-product tradeoffs, multi-tenant architecture, and build-vs-integrate decisions.
- Ability to influence C-level executives, technical architects, and partner ecosystems.
Ecosystem Fluency
- Strategic understanding of the broader ecosystem: when to partner, differentiate, or disrupt.
- Familiarity with platform strategies of major tech players (e.g., AWS Bedrock vs. Databricks Lakehouse vs. Salesforce AI Cloud).
- Bonus: M&A or partnership experience within the data/AI ecosystem.
Why This Role is Unique
- Greenfield opportunity to shape a category-defining AI platform with full executive backing.
- Build infrastructure that powers real-time, autonomous decision-making in the world’s largest enterprises.
- Define how Agentic AI and Knowledge AI come alive through connected data, workflows, and responsible governance.
- Work alongside world-class AI researchers, product thinkers, and engineering leaders.
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
- Stay informed on emerging technologies and trends in big data, AI/ML, and analytics.
- Champion with the products of AI-ready technologies such as automated data labeling tools, data versioning systems, and explainable AI frameworks.
- Evaluate and incorporate emerging technologies to enhance data readiness for AI, such as synthetic data platforms or edge AI solutions.
Governance & Compliance
- Ensure data products take into practices that adhere to global regulations (e.g., GDPR, CCPA) and ethical AI standards.
- Products enforce governance policies for data quality, lineage, and versioning critical for AI integrity.
- Collaborate with legal and compliance teams to manage risks associated with sensitive or biased data during the product development and sales process.
Qualifications
Education & Experience
- Bachelor’s degree in Computer Science, Data Science, AI, or a related field; Master’s degree in Databases or Machine learning technology and PHD would be ideal.
- 10+ years of experience in data product management, AI, or analytics, with at least 5 years in a senior leadership role.
- Expertise in product management frameworks and agile development methodologies.
Key Metrics for Success
- Revenue growth or cost savings driven by AI Agentic products.
- Migration of turn-key apps like U and Q to AI Agentic platform.
- Product adoption rates and customer satisfaction scores.
- Time-to-market for new product features.