New opportunity

AI PM

Libra AI, Inc · Bengaluru, India

About this role

Libra AI, Inc is hiring an on-site AI PM in Bengaluru to work directly with its founding team. This is a 0→1 product role with end-to-end ownership, from shaping ideas to shipping AI-native features.

Responsibilities

Own the product from initial idea through shipped feature, working closely with engineering to design and deliver AI-native workflows.
Prototype quickly using tools such as Claude and Lovable, and validate ideas independently.
Set product direction based on user behavior while maintaining a high standard for UX, detail and product quality. Work with speed and ownership.

Requirements

2–4 years of product management experience; operations or analyst experience does not substitute for product management experience.
Strong understanding of AI systems, including LLMs, agents and orchestration; sufficient technical fluency to collaborate closely with engineers.
Ability to prototype and validate ideas independently, strong product judgment, attention to detail and initiative.
Availability to join immediately or within two weeks at most.

Preferred

Experience building or shipping AI products, strong design and UX judgment, and experience in fast-paced 0→1 environments.

The role offers direct work with the founder, full ownership from day one, top-of-market cash compensation and meaningful equity; no specific compensation amount is stated. Applicants are asked to send a resume and a two-line introduction by DM.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Product managementAI systemsLarge language modelsAI agentsAI orchestrationPrototypingClaudeLovableUser experience designProduct validationCollaboration with engineering

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Own the 0→1 product cycle
The role owns work from initial idea to shipped feature and calls for speed and high ownership while working with the founding team.
Design AI-native workflows with engineering
Close engineering collaboration is central. The posting calls for technical understanding of LLMs, agents and orchestration to shape and ship workflows.
Prototype and validate independently
Rapid prototypes using tools such as Claude and Lovable are an explicit duty, alongside independently testing whether ideas are worth pursuing.
Use behavior to set direction
Product direction should follow user behavior rather than opinions, making evidence from actual use important to prioritization.
Protect UX and product quality
The posting emphasizes product taste, attention to detail and a high standard for UX and quality, even while shipping quickly.

Likely challenges to think through

Speed versus validation
Preparation hypothesis: fast 0→1 shipping may leave limited time to test an idea before committing engineering effort. Prepare a concise approach for prototyping, observing user behavior and deciding whether to build, revise or stop.
AI ambition versus workable workflows
Preparation hypothesis: an attractive agent-based concept may be harder to deliver as a dependable user workflow. Prepare to explain how to scope an LLM or agent prototype with engineers and identify what needs testing before release.
Ownership versus polish
Preparation hypothesis: end-to-end responsibility and rapid delivery may force trade-offs between launch scope and UX detail. Prepare a concrete way to distinguish essential experience-quality issues from improvements that can follow a first release.

Practice your answers

Technical practice

How would you take a 0→1 AI product idea from initial concept to a shipped feature?

What to coverThe role owns product end-to-end, from idea to shipped feature. Suggested preparation: explain how you would define the user problem, narrow the first workflow, test an early version and decide what is ready to ship.

When would you use an LLM alone rather than an agent-based workflow?

What to coverThe posting calls for understanding LLMs, agents and orchestration. Suggested preparation: compare a bounded model interaction with a multi-step agent, including the added flexibility and complexity of the latter.

How would you design the steps of an AI-native workflow with engineering?

What to coverThe role involves working closely with engineering to design and ship AI-native workflows. Suggested preparation: describe the user journey, where model or agent actions belong, and how users would review or correct an output.

How would you prototype an AI feature quickly and decide whether to develop it further?

What to coverThe posting names Claude and Lovable as examples of prototyping tools and expects independent validation. Suggested preparation: outline a small prototype, the assumption it tests and the evidence that would justify further investment.

What user behavior would lead you to change product direction?

What to coverThe role calls for direction based on user behavior rather than opinions. Suggested preparation: distinguish what users say from what they do, and explain how you would investigate behavior before changing the product.

How would you evaluate the quality of an AI-generated result?

What to coverThe posting emphasizes AI systems and a high standard for product quality. Suggested preparation: describe task-specific examples, criteria for a useful result and how you would identify failures that matter to users.

How would you investigate an AI workflow that produces inconsistent results?

What to coverThe role requires technical fluency to work closely with engineers on AI-native workflows. Suggested preparation: separate possible issues in the input, model behavior and workflow steps, then explain how you would reproduce a failure and assess a fix.

What UX details matter most when users interact with an AI feature?

What to coverThe posting explicitly emphasizes UX, detail and product quality. Suggested preparation: discuss how users understand what the feature is doing, recognize uncertainty and recover when a result is not useful.

How would you make speed-versus-quality trade-offs before shipping?

What to coverThe role combines fast shipping with high ownership and attention to detail. Suggested preparation: explain which issues you would resolve before release, which you might monitor afterward and what evidence would inform that choice.

Behavioural practice

Tell me about a product feature you owned from idea to launch.

What to coverEnd-to-end ownership is central to this role. Suggested preparation: use a real situation, your decisions and actions, and the result; distinguish your contribution from the team's.

Tell me about a time user behavior challenged a product assumption.

What to coverThe posting calls for product direction grounded in user behavior. Suggested preparation: describe the original assumption, the behavior you observed, what you changed and the result.

Describe a time you worked through a difficult product decision with engineers.

What to coverThe role requires close engineering collaboration and technical fluency. Suggested preparation: explain the situation, the trade-off you discussed, your actions and the outcome without claiming technical work you did not do.

Tell me about a time you moved a product forward without waiting for direction.

What to coverThe posting emphasizes high agency, speed and ownership in a 0→1 setting. Suggested preparation: give a real example of the ambiguity you faced, the initiative you took and the result.

Describe a time you caught an important UX or quality issue before shipping.

What to coverAttention to detail and product quality are explicit priorities for this role. Suggested preparation: explain how you found the issue, what action you took under any time constraints and what happened afterward.

Sharpen your knowledge

Topics to prepare

LLMs, agents and orchestration
The posting explicitly calls for strong understanding of all three. Revise by explaining when a workflow needs a single LLM interaction versus an agent, how steps would be coordinated, and what could fail at each handoff.
0→1 product ownership
The role owns product from idea to shipped feature. Revise by outlining how you would turn an unspecified AI-native workflow idea into a scoped first release, including key decisions and trade-offs. The example workflow is a preparation exercise, not a stated company project.
Direction from user behavior
The posting says product direction should follow user behavior rather than opinions. Revise by comparing two hypothetical usage patterns and explaining what each would lead you to investigate or change; the patterns are illustrative, not company data.
UX and product-quality judgment
The posting emphasizes UX, detail and product quality. Revise by examining a hypothetical AI workflow for unclear steps, confusing outputs and failure states, then explain which issue you would address first and why.

Skills to revise

Rapid AI prototyping
The posting names Claude and Lovable as examples of tools for fast prototyping. As practice, build a small AI-native workflow prototype with one of them and note what it demonstrates and what remains untested.
Independent idea validation
The role calls for independently prototyping and validating ideas. Practice writing a testable assumption for the prototype, observing how someone uses it, and deciding what evidence would justify revising or stopping the idea. This test is a preparation suggestion, not a prescribed company process.
Engineering-ready feature scoping
The PM will work closely with engineering to design and ship AI-native workflows. Practice drafting a short feature brief with the user flow, expected AI behavior, edge cases and open technical questions for an engineer to challenge.
Detail-focused workflow review
The posting asks for high attention to detail and product quality. Practice walking through your prototype step by step, recording unclear wording, broken transitions and unexpected AI responses, then prioritizing fixes before a hypothetical release.

Your preparation checklist

Build an end-to-end PM walkthrough
Prepare a concise account of a product you owned, if available: the initial problem, decisions, engineering collaboration, shipped feature, and observed results. Distinguish PM ownership from operations or analyst work; the posting asks for 2–4 years of PM experience.
Sketch an AI-native workflow
As a preparation exercise, choose a hypothetical user problem and produce a one-page flow from user input to AI output. Mark key UX decisions, failure states, and what you would need to learn before building; do not present it as Libra AI’s product.
Prepare a rapid prototype demonstration
If feasible, build a small prototype using Claude, Lovable, or a comparable tool. Be ready to show what you built independently, what you tested, what changed, and what remains unvalidated.
Explain the technical trade-offs
Prepare a brief diagram and talking points for your proposed workflow: where an LLM, agent, or orchestration would help, where simpler logic might suffice, and which decisions you would resolve with engineers.
Show how evidence would change direction
Bring an example, if available, of using user behavior to revise a product decision. Note the signal you observed, the decision it informed, and a specific UX detail you improved; otherwise frame this as a validation plan for your exercise.
Prepare questions and application materials
Draft questions for the founding team about the first user problem, evidence used to set direction, and how product and engineering share decisions. Prepare the requested resume and two-line DM introduction, and be ready to state your availability for the on-site Bengaluru role and the immediate-to-two-week joining window.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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