Our client is on a mission to transform how professional organizations, industry associations, and expert communities solve problems, deliver value, and engage their members. They believe that with the right blend of innovation, leadership, and technology, these communities can play an even more powerful role in shaping industries and society.Our client is building intelligent, human-centered solutions that help these organizations thrive—combining the promise of AI with a deep understanding of how people and communities work. Founded by experienced leaders in technology, product strategy, and go-to-market execution, they are ready to scale. They are currently seeking an experienced Head of AI Product Management to join their growing team.As the Head of AI Product Management, you will:
Spearhead Client’s product vision and execution for our neuro-symbolic, human-in-the-loop (HITL) vertical AI platform.
Shape the product vision, strategy, and priorities, ensuring alignment with company goals and market needs.
Guide cross-functional teams from concept through launch, integrating cutting-edge AI (including agentic AI and LLM technologies) with user-centric design.
Champion Client‘s neuro-symbolic AI approach – blending machine learning with symbolic reasoning – and leverage HITL feedback loops to continuously refine product performance and trustworthiness.
Be held accountable for delivering AI products that are innovative, scalable, ethical, and impactful in real-world industry verticals.
Role Requirements include:
Commercial Experience: 7+ years in product management, with at least 3–5 years specifically managing AI or machine-learning based products (e.g., AI SaaS, data/analytics platforms, or intelligent enterprise software)
Demonstrated Success: You have taken a product from idea to launch, especially in a startup or fast-moving environment. Prior leadership of product teams or cross-functional product initiatives is required.
Domain Knowledge: Strong understanding of AI/ML technologies and their commercial applications – you should grasp the basics of model development, data pipelines, and AI use-cases. Hands-on familiarity with concepts like NLP, computer vision, or recommender systems in a product context is expected. Experience with human-in-the-loop systems or AI model lifecycle management is highly advantageous, aligning with Sapience’s approach.
Industry Background: Experience working in B2B software or enterprise-focused products. Knowledge of challenges in deploying AI in enterprise settings (such as data privacy, model explainability requirements, integration with legacy systems) will help you anticipate and address client needs.
Leadership & Soft Skills: Proven ability to lead and influence teams. You should have excellent collaboration skills, having driven results in a matrixed organization. Exceptional communication and presentation abilities are a must – from crafting compelling product visions for execs to conducting demos for clients, and writing clear requirements for engineers
Skills & Competencies
Product Strategy & Execution: Ability to formulate a bold product vision and break it down into executable steps. Highly skilled in product road mapping, requirements writing, and backlog management, with attention to detail and quality.
AI/ML Literacy: Strong familiarity with AI/ML concepts and the software development lifecycle.
Analytical & Data-Driven: Excellent analytical skills with a metrics-driven approach to decision making. Proficient in using analytics and user data to derive insights; comfortable defining KPIs and interpreting A/B test results or usage funnels to iterate on the product.
Customer Empathy & UX Sense: Deep empathy for end-users and enterprise customers. You champion the user’s perspective in all decisions, ensuring the AI product remains intuitive and solves meaningful problems. A good eye for user experience and design; able to work closely with UX/UI teams to craft seamless interactions even around complex AI functionality.
Agility & Adaptability: Comfortable in a startup environment where priorities can shift.
Tools & Services Experienced With
Product Management Tools: Proficiency with tools like Jira, Trello, or Asana for backlog and project tracking. Experience using road mapping software (Aha!, Productboard, etc.) to communicate plans. Familiarity with collaboration and documentation tools (Confluence, Notion, Workspace) to create specs and reports.
Analytics & Feedback Tools: Hands-on experience with product analytics and user feedback platforms. For example, usage of Mixpanel, Google Analytics, Amplitude or similar to monitor product metrics; experience running user surveys or using feedback tools like UserVoice. Some knowledge of A/B testing frameworks and experimentation platforms is beneficial for data-driven feature rollout.
AI/ML Ecosystem Familiarity: While not coding, you have exposure to AI/ML tools and cloud services. This could include understanding of platforms like AWS (SageMaker), Google Cloud AI, Azure AI, or familiarity with OpenAI/Anthropic APIs, etc. You know enough to discuss integration of an AI service or to evaluate the feasibility of a machine learning feature. Knowledge of MLOps concepts and tools (e.g., model deployment workflows, monitoring tools like MLflow) is a plus, enabling close collaboration with the ML Engineering team.
Design/UX Tools: Experience working with design outputs – comfortable reviewing Figma or Adobe XD files, for instance, and providing feedback. You might not design yourself, but you can navigate design prototypes and ensure the user experience aligns with product requirements.
CRM and Customer Success Systems: Understanding of systems like Salesforce or HubSpot and how product features funnel into customer usage and feedback. Experience aligning CRM data or support ticket systems (Zendesk, Intercom) to glean product improvement ideas.
Office & Presentation: Highly proficient with standard business tools (Excel/Sheets for analysis, PowerPoint/Slides for presentations). Able to create clear presentations of product strategy or roadmap for executives and customers. If you have used more advanced data tools (SQL, Tableau) or prototyping tools (Marvel, InVision) that’s a plus, demonstrating a breadth of toolkit.
Prior Industry Experience
Enterprise Software & SaaS: You have spent a significant part of your career in software companies delivering solutions to enterprise clients. This means you understand reliability, scalability, and support requirements of enterprise-grade products. You likely have experience aligning with enterprise sales teams and understanding B2B client expectations (SLAs, onboarding, security reviews, etc.).
AI or Data Technology Companies: Your background includes roles at companies where AI or data analytics is a core product. For example, you may have worked at an AI platform company, a big cloud provider’s AI division, or a startup building AI-driven applications. This experience ensures you can handle the nuances of AI product development (like iterative model improvements and data dependency) and communicate value in a space that can be hype-driven.
Top Tech or Innovative Firms: Preferably, you’ve been part of an innovative tech culture – possibly at one of the industry leaders mentioned (OpenAI, Google, Microsoft, Meta, Apple, Netflix, Anthropic, DeepMind, etc.) or a well-regarded startup. Being in such environments often means you have seen best-in-class practices and can bring that rigor and creativity to Sapience.
Vertical Domain Exposure: Because Sapience’s AI agents are vertical-focused, any domain expertise you bring (be it finance, healthcare, legal, etc.) can be useful. For instance, if you helped build an AI product in finance, you’re familiar with compliance and data issues in that space. While domain expertise is not strictly required, having launched products in one or more verticals will help you quickly adapt Sapience’s platform to industry-specific needs.
Human-Centered AI Projects: Experience in projects that combined human expertise with AI is valuable. Perhaps you worked on a product with a crowd-sourced labeling component, or a decision support tool where humans and AI interacted. This experience directly aligns with Sapience’s HITL philosophy and will help in designing effective human-AI workflows.
Preferred EducationA strong educational foundation underpins this role. A Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Human-Computer Interaction, or Engineering disciplines or related relevant fields (or equivalent practical experience) is required. An MBA or advanced degree is a plus, reflecting strategic business training, but not required ifcompensated for by strong real-world product success. If interested in learning more about this dynamic new opportunity, please send your resume to [email protected].
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