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Tessera Labs is seeking a Product Manager to join its product organization and help redefine how enterprises adopt and operationalize AI. The company builds multi-agent AI systems that automate complex business workflows across enterprise platforms including SAP, Salesforce, Workday, Snowflake, and MuleSoft.
This is a hands-on individual contributor role with high ownership and visibility. You will own AI-driven features end-to-end, from problem discovery and model-informed product strategy through execution, iteration, and measurable business impact. The role sits at the intersection of product management, data science, engineering, and customer outcomes.
Key responsibilities include conducting market and customer analysis to identify pain points; defining product strategy, roadmap, and success metrics for assigned areas; identifying high-value AI use cases and prioritizing based on ROI, feasibility, and time-to-value; working closely with customers, sales, and solutions teams to understand real-world workflows; building clear product requirements and translating them into actionable engineering backlogs; partnering with engineering in an agile environment; collaborating with sales, marketing, and GTM teams on positioning and launches; and working directly with customers during implementations to ensure adoption.
Required qualifications include experience building and scaling B2B products, strong background in ERP or enterprise platforms (Salesforce, SAP, Oracle, ServiceNow), AI-first product mindset with experience launching AI-driven products, ability to build high-fidelity requirements using tools like Cursor or Replit, excellent communication skills, strong analytical skills with bias toward measurable outcomes, strategic thinking and prioritization ability, deep understanding of agile methodologies, strong cross-functional collaboration skills, and engineering awareness (comfortable discussing platforms and architectures without hands-on coding).
Nice-to-have qualifications include experience with workflow engines, data harmonization across enterprise platforms, industry-specific solutions, code remediation or platform modernization, strong product storytelling and go-to-market experience, and UX sensibility for AI-driven user experiences.
