Quabrick Labs Search Verified employer
Actively hiring · Posted 9d ago · JEV2610EA03C6CCD9
About the Role
We are seeking a Staff Product Manager / Group Product Manager to spearhead the vision, strategy, and execution of our Vertical AI & Agentic Platform. In this role, you will lead the development of enterprise-grade AI solutions, laying the groundwork for our AI Agentic Platform, driving LLM selection, and integrating predictive, generative, and agentic workflows into our core SaaS ecosystem.
We are a $4B+ valuation enterprise SaaS unicorn growing at 50% YoY with over $200M in ARR, re-imagining workflows across a massive, under-penetrated total addressable market (TAM).
Key Responsibilities
AI Vision & Strategy: Define the multi-year roadmap and execution plan for cutting-edge Vertical AI and Agentic solutions across web and mobile.
2.*AI-First User Experience: *Re-imagine end-to-end user journeys and enterprise workflows to optimize for an AI-native world across complex operational SaaS stacks.
Product Execution: Craft detailed product requirements, specs, and narratives, collaborating closely with Data Engineers, ML Engineers, and Data Scientists to move from prototype to production-grade capabilities.
4.Platform Architecture & LLM Strategy: Make strategic decisions on LLM selection, RAG frameworks, and agentic architectures balancing speed, accuracy, and enterprise trust.
Data-Driven Insights & Analytics: Build the analytics and observability layers needed to track AI model performance, user adoption, and ROI.
Internal AI Adoption & Ecosystem: Evaluate partnerships and drive POCs with AI startups, cloud vendors, and data providers to accelerate product innovation and internal productivity.
Skills & Qualifications
7+ years of total Product Management experience in high-growth B2B or B2B2C SaaS.
3+ years of hands-on experience leading AI/ML-focused product initiatives, with a track record of launching production-grade AI solutions at scale.
Technical Depth in AI: Deep understanding of Generative AI, LLMs, Agentic frameworks, Predictive AI, and underlying data pipelines.
Engineering Collaboration: Proven experience driving requirements for Data Science, Data Engineering, and ML Engineering teams.
Enterprise SaaS Domain: Prior background in complex vertical SaaS platforms (ERP, CRM, DMS, Accounting, Commerce, or Analytics).
Good to Have
Technical degree in Computer Science, Machine Learning, Statistics, or a related field.
Familiarity with modern AI tools, infrastructures, and cloud platforms (e.g., LangChain, Pinecone, AWS SageMaker, Azure ML, Vertex AI, Databricks).
Track record of building and scaling high-performing product teams in fast-paced startup environments.
