AI Product Manager

About Testsigma

Testsigma is building the world's first Quality Intelligence Platform -- the infrastructure layer that sits between every code change and every confident release. The platform uses AI agents to author, execute, and heal tests autonomously, giving engineering leaders a calibrated signal of whether a release is safe to ship. Software is being rewritten by AI faster than at any point in its history. Code that used to take a quarter now ships in a week. The gap between how fast software is built and how long it takes to trust that software is the largest unsolved problem in engineering today -- and it is the problem Testsigma exists to close. Design partners are live, the product is proven, and the market is moving toward autonomous testing. The companies that build the substrate for release intelligence over the next 12 months will own the next decade of software testing.

Role Summary

This is a senior individual contributor Product Manager role and a direct member of the founding product team. The role owns a major surface of the autonomous testing platform end-to-end, spanning both AI-native capabilities and the platform foundations that make them real. It is one of the most strategically important PM roles on the team. The right candidate takes a vague directive, turns it into a sequenced roadmap, prototypes first-cut solutions independently, engages directly with enterprise customers, and ships on a weekly cadence -- all without waiting to be invited into the room where the decision is being made.

Key Responsibilities

Product Ownership

• Context and Signal Infrastructure: Own the intake, schema, and ingestion layer that converts customer signals -- PRDs, code, analytics, support tickets, session recordings -- into a compounding knowledge layer that powers downstream AI capabilities.
• Agent Surfaces: Define and ship AI agents that author, plan, execute, heal, and analyze tests on behalf of customers. Design the human-in-the-loop touchpoints that keep agents trustworthy and production-ready.
• Coverage and Release Intelligence: Build coverage taxonomy, requirement-to-test mapping, and release-readiness signals that engineering leaders can act on during sprint reviews and board updates.
• Developer Experience and SDLC Integration: Own the authoring loop inside AI-native developer tools (Claude, Cursor), dual-format save and migration, and distribution via Skills, MCP server, and CLI.
• Executive Reporting: Design dashboards and signals that surface release-readiness data a VP of Engineering can present in a board update.
• Platform Foundations: Own onboarding flows, integrations with Jira, GitHub, Confluence, Linear, Notion, Mixpanel, Amplitude, Zendesk, Hotjar, and other tools in the customer stack. Turn capability into a product a new customer can deploy without a six-week services engagement.
• Product Roadmap and Narrative: Translate strategy from a deck into a sequenced set of surfaces customers can buy. Write enablement materials. Join pre-sales and renewal calls directly.

First-Year Deliverables

• An intake flow that lets a new customer connect Jira, GitHub, and one analytics source in under 30 minutes and see a useful output within one week.
• A coverage view an engineering manager can open during a sprint review to make a real decision -- not a vanity dashboard.
• A release-readiness score a VP of Engineering trusts enough to include in a board update.
• Agent surfaces customers allow to run on production-adjacent systems -- measured by autonomy rate, not just usage.
• A Skills + MCP + CLI distribution surface that at least one AI-native developer tool integrates with as a launch partner.
• A migration path from legacy automation suites (Selenium, Playwright, previous Testsigma TMS) that lets customers adopt the new platform without discarding existing tests.

Customer Engagement

• Join strategic customer conversations where roadmap or positioning decisions are being made.
• Take escalations directly when the issue concerns what was built or whether it should have been built.
• Participate in pre-sales conversations where enterprise buyers want to engage the PM who owns the surface, not just a sales engineer.
• Review every churn note, NPS comment, and onboarding drop-off log proactively -- not by waiting for forwarded summaries.

Required Qualifications

• Experience: 4 to 9 years of total professional experience, with at least 3 years in product management. Engineer-turned-PM is the strongly preferred profile. A pure MBA-to-PM path with no engineering background is a yellow flag for this specific role.
• AI-Native Execution: Demonstrated hands-on experience building with AI tools -- Claude, Cursor, Lovable, LangChain, LangGraph, or comparable -- within the past 12 months. Must be able to describe what was built, the specific technical decisions made, and what broke. Reading about MCPs does not qualify.
• Prototyping Ability: Can independently produce a first-cut wireframe, clickable prototype, or working code that an engineer can take over. Does not require a designer to make a point of view legible.
• Engineering Fluency: Can read a pull request, whiteboard a state machine, and pair-debug a flaky test. Will not be hired to do engineering work but must operate credibly in the same room as engineers.
• 0-to-1 Product Ownership: Has taken a net-new product to general availability -- not a feature on a mature product. Can describe the full arc including the last 20 percent that was hardest to finish.
• Direct Customer Experience: Has navigated hard customer conversations personally: renewals, escalations, deal-saving demos, churn calls. The CS team forwarding customer feedback does not satisfy this requirement.
• Written Communication: Can write clearly and precisely without AI editing. The take-home assignment evaluates this directly.
• Office Presence: Available for 5 days per week in the Koramangala office and comfortable operating across time zones to support an approximately 80 percent North American customer base.
• Tenure and Stability: Actively seeking 3 to 5 years of commitment at the next role. Tenure patterns will be reviewed and any exits under 18 months in the last five years will be discussed explicitly.

Preferred Qualifications

• Prior experience in software testing, QA, developer tools, or developer infrastructure.
• Familiarity with Selenium or Playwright at the framework level, including failure modes -- not just the happy path.
• Understanding of how knowledge graphs differ architecturally from search and retrieval-augmented generation (RAG) systems.
• Experience repositioning a product from QA-buyer to engineering-leadership buyer -- moving the deal up the org chart from a QA Manager to a CTO or VP Engineering.
• Experience designing or evaluating LLM-as-judge frameworks, evaluation harnesses, or confidence scoring systems.
• Prior founder experience or strong founder-adjacent experience as an early product hire at a seed-to-Series-B company.

CORE COMPETENCIES

Execution

• Converts ambiguous directives into a sequenced roadmap within days, not weeks. Returns from unstructured exploration with customer interviews, flow sketches, draft schemas, and specific asks -- not requests for more meetings.