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we are seeking a highly skilled AI Product Intelligence Lead to modernize and elevate the Product Management function by building an AI-driven product knowledge platform. This role focuses on aggregating and unifying organizational data (engineering, product, competitive, customer) into a structured data lake, and developing an internal AI assistant that empowers the Product organization with real-time insights, competitive analysis, roadmap support, and decision-making acceleration.
This position sits at the intersection of AI, data engineering, product strategy, and organizational knowledge management. You will function as the AI engine behind our company Product Management - turning scattered information into structured intelligence and building the tooling that allows PMs to think faster, act smarter, and execute at scale.
Key Responsibilities
1. Build the company Product Data Lake
Ingest and normalize data from internal systems including:
Git / GitHub / GitLab (code evolution, releases, components, architecture)
Jira (epics, features, sprint data, backlog health, execution trends)
Confluence (specs, design docs, product decisions, reviews)
Connect and harvest relevant external data:
Competitor websites and public documentation
Analyst reports, industry standards, RFCs
Media, blogs, financial/market data, community repos
Establish pipelines for continuous ingestion, cleaning, tagging, and semantic indexing.
Define and enforce taxonomies, metadata schemas, and embedding strategies for structured and unstructured product information.
2. Build an AI Assistant for the Product Management Function
Design and maintain a secure, internal company PM chatbot capable of:
Understanding our company architecture, products, roadmaps, modules, and features
Generating roadmap proposals based on business goals, competitive gaps, and engineering capacity
Providing competitive analysis, feature comparisons, and market positioning
Summarizing design docs, Jira epics, technical specs, PRDs, RFCs, and Confluence pages
Acting as an AI peer/consultant for PMs: drafting PRDs, feature breakdowns, risks, GTM summaries, and opportunity reports
Implement multi-source retrieval (RAG), long-context reasoning, and fine-tuning strategies where applicable.
Collaborate with Engineering to ensure data security, access control, and compliance.
3. AI-Driven Product Intelligence & Competitive Strategy
Automatically identify gaps vs. competitors and recommend differentiators.
Use AI to synthesize market intelligence and propose strategic moves.
Build dashboards for product, competitive, and risk insights.
Create automated alerts for competitor releases, standards changes, and relevant industry updates.
4. Cross-Functional Collaboration
Work closely with Product Managers, R&D, Architecture, CTO Office, Sales Engineering, and Leadership.
Translate business goals into AI capabilities and intelligence-driven tooling.
Train PMs on using AI tools effectively and integrate AI into their daily workflows.

our company is the defining technology company of the ai era, driving innovation through exceptional technology and talent. in this role, you will help shape the evolution of our doca networking software stack - the accelerated infrastructure framework powering ai factories and cloud environments. you will drive software innovation from vision to real-world impact, influencing some of the most advanced computing systems in the world. as part of the doca product group, you will lead software strategy for connectx nic and bluefield dpu platforms - key pillars of our data center and ai networking strategy - helping build the intelligent infrastructure of tomorrow.
what you'll be doing:
lead the product-strategy for doca networking stack and products across their life-cycle: from vision and inception, through detailed customer & ecosystem requirements, roadmap crafting, market introduction, growing into in-scale delivery, and product improvement cycles.
orchestrate a unified technical strategy between ai product teams, engineering teams, and customers to advocate the use of doca libraries & microservices, and to develop new doca apis and services for new deployments.
drive multidisciplinary engineering and architecture teams to establish priorities and define precise, actionable requirements for breakthrough projects.
forge strong partnerships with customers and ecosystem partners - actively listening to their technical needs, delivering expert guidance, and supporting successful, large-scale ai (and other) deployments that drive their strategic goals.
create use cases and reference applications to demonstrate product value to technical and executive audiences.
gather insights to define future products, including analysis of complementary and competitive products and customer feedback.