New opportunity

AI Product Manager

Seven AI · Boston, United States

About this role

7AI seeks an AI Product Manager to design and launch AI-driven security products that help organizations detect, analyze, and respond to evolving cyber threats. The role combines product ownership with hands-on prototyping, from concept validation through production deployment.

Responsibilities

  • Prototype security features with AI coding tools such as Cursor and Copilot to test product ideas and technical feasibility.
  • Use internal data, threat intelligence, and market signals to identify opportunities and validate product direction. Turn ambiguous problems into requirements and shipped solutions.
  • Balance security effectiveness, performance, usability, and operational complexity. Work with engineering, security researchers, and business stakeholders on delivery.
  • Identify opportunities to automate team workflows with agentic AI and internal tools, and contribute to AI-driven detection, response, and analysis capabilities.

Requirements

  • Bachelor’s degree or higher in a technical field, or equivalent experience; 5+ years in product development or a hybrid role, including building AI-powered applications.
  • Hands-on experience with LLMs and AI coding tools for prototyping and analysis, and with GenAI systems such as LLM integrations, RAG pipelines, agentic workflows, or evaluation frameworks.
  • Demonstrated ability to take products from concept to launch with a focus on user impact.

Preferred

Experience building security or infrastructure products, including detection systems, monitoring tools, or distributed services; a portfolio of AI-native rapid prototyping projects; experience using product analytics and telemetry for decisions; and familiarity with cloud infrastructure such as AWS and data visualization tools.

Location and compensation: Boston, MA; hybrid, full-time. Annual salary range is $200,000–$260,000, with equity offered.

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

Skills for this role

Product developmentProduct managementRapid prototypingCursorCopilotLarge language modelsGenerative AILLM integrationRAGAgentic workflowsAI evaluationCybersecurityThreat intelligenceWorkflow automationProduct analyticsTelemetryAWSData visualization

Your skill match

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Own the path from concept to launch

    The role owns product definition from ambiguous security problems through requirements, deployment, and user impact. Prepare a concise example of how you validated a problem, made scope decisions, and shipped a product.

  • Prototype AI-powered security features

    The posting calls for hands-on prototyping with AI coding tools such as Cursor or Copilot, alongside experience with LLM-based systems. Prepare to explain what a prototype tested, what it could not prove, and how its results informed a product decision.

  • Choose opportunities using evidence

    Internal data, threat intelligence, and market signals are named inputs for identifying and validating opportunities. Prepare to show how you would compare conflicting signals and decide what to investigate first.

  • Make security-product trade-offs explicit

    The role must balance security effectiveness, performance, usability, and operational complexity across detection, analysis, and response capabilities. Prepare to explain a trade-off you made, including what you measured and what you chose not to optimize.

  • Coordinate delivery and identify automation opportunities

    The posting calls for partnership with engineering, security researchers, and business stakeholders, plus identifying cross-team workflow automation using agentic AI and internal tools. Prepare an example of aligning different perspectives around a deliverable.

Likely challenges to think through

  • Turning a fast prototype into a dependable capability

    A likely challenge is that a prototype can establish feasibility without proving production behavior or user value. Prepare to distinguish the evidence needed at each stage and explain when you would refine, release, or stop an idea.

  • Improving detection without overwhelming users or systems

    A likely trade-off in the advertised real-time security work is stronger threat coverage versus performance, usability, and operational burden. Prepare a decision framework that considers these effects together rather than treating detection quality alone as success.

  • Selecting where agentic automation helps

    Cross-team workflow automation is a stated opportunity, but the posting does not identify which workflows should change. Prepare to explain how you would assess a candidate workflow’s value, reliability, and need for human oversight before prioritizing it.

03

Practice your answers

Technical practice

How would you identify and validate an opportunity for an AI-driven security feature?

What to coverSuggested answer focus: Explain how you would compare internal data, threat intelligence, and market signals, then test whether the problem matters to users. Distinguish evidence of demand from evidence that a proposed solution is technically feasible.

How would you turn an ambiguous security problem into product requirements?

What to coverSuggested answer focus: Define the user problem, intended outcome, scope, and acceptance criteria before committing to a solution. Explain how you would resolve open questions with engineering and security researchers.

How would you use an AI coding tool to test a security-product idea quickly?

What to coverSuggested answer focus: Describe a narrowly scoped prototype, what feasibility question it answers, and how you would check AI-generated code. Separate prototype evidence from the reliability and maintainability needed for production.

When would you use an LLM integration or RAG pipeline for threat analysis?

What to coverSuggested answer focus: Compare direct LLM use with retrieval grounded in relevant information, including how source quality affects the output. Discuss limitations such as incorrect conclusions and how users could inspect supporting evidence.

How would you evaluate an AI-powered detection or analysis capability?

What to coverSuggested answer focus: Propose evaluations that reflect the intended security task, including useful results, missed threats, and false alarms. Explain how you would test with representative data and decide whether the results justify launch.

What trade-offs would you make in a real-time threat detection and response feature?

What to coverSuggested answer focus: Balance security effectiveness and response speed against performance, usability, and operational complexity, as the posting specifies. Explain which user or operational evidence would change your priorities.

Where could an agentic AI workflow help automate a team process, and what should remain under human control?

What to coverSuggested answer focus: Choose a bounded workflow and describe its inputs, actions, and checks before expanding its autonomy. Consider the cost of incorrect actions and how the team would detect and correct failures.

How would you use product telemetry to decide whether a security capability is working?

What to coverSuggested answer focus: As preparation for a listed nice-to-have, connect usage and outcome measures to a specific product decision rather than reporting activity alone. Note how you would interpret signals alongside user feedback and security effectiveness.

How would you move an AI security prototype from validation to production deployment?

What to coverSuggested answer focus: Identify the evidence needed to proceed, the requirements that must become explicit, and the handoff or collaboration needed with engineering and research. Address how you would check performance and operational readiness without assuming a particular architecture.

Behavioural practice

Tell me about a time you brought clarity to an ambiguous product problem.

What to coverSuggested answer focus: Give the situation, your specific actions to define the user problem and requirements, and the resulting decision or shipped outcome. Explain how you handled uncertainty rather than presenting the final requirements alone.

Describe a time you resolved competing priorities across technical and business stakeholders.

What to coverSuggested answer focus: Set out the situation, who disagreed, and the actions you took to surface evidence and trade-offs. State the result and what it meant for delivery or users.

Tell me about a product you took from concept to launch.

What to coverSuggested answer focus: Use a real example to show the situation, your ownership across validation and delivery, and the result for users. Be precise about your contribution and how you assessed impact.

Describe a time new evidence changed your product direction.

What to coverSuggested answer focus: Explain the original assumption, the data or feedback that challenged it, and the action you took. Close with the result, including what you learned if the revised direction did not succeed.

Tell me about a rapid prototype that exposed a technical limitation.

What to coverSuggested answer focus: Describe the situation and the feasibility question, then explain what you built or tested and how you shared the finding. State the resulting product decision and distinguish what the prototype proved from what remained uncertain.

02

Sharpen your knowledge

Topics to prepare

  • Security product lifecycle

    The posting calls for owning capabilities from concept validation through production deployment, with a focus on user impact. Suggested revision exercise: outline the decisions and evidence needed at each stage for a hypothetical threat-analysis feature.

  • Opportunity validation

    The role uses internal data, threat intelligence, and market signals to identify opportunities. Suggested revision exercise: compare three hypothetical signals and explain which product problem you would investigate first, including what evidence could change your mind.

  • Security product trade-offs

    The posting explicitly calls for balancing security effectiveness, performance, usability, and operational complexity. Suggested revision exercise: rank these considerations for a hypothetical detection feature and explain the trade-off you would accept.

  • GenAI system design

    Experience with GenAI systems is required; the posting gives LLM integrations, RAG pipelines, agentic workflows, and evaluation frameworks as examples. Suggested revision exercise: sketch one design, its failure modes, and how you would evaluate whether it helps users.

  • Detection, analysis, and response

    These are the security capabilities the role would help develop; prior security-product experience is listed as a nice-to-have. Suggested revision exercise: map a hypothetical threat signal through detection, analyst review, and response, noting where an AI feature could help or cause harm.

Skills to revise

  • AI-assisted feature prototyping

    The role explicitly calls for prototyping security features with AI coding tools such as Cursor or Copilot. Suggested exercise: use one of those tools to build a small prototype around sample threat data, then document what it does and what feasibility questions remain.

  • Writing actionable product requirements

    The posting calls for turning ambiguous problems into clear requirements and shipped solutions. Suggested exercise: write a one-page requirement for a hypothetical security feature with a user problem, scope, acceptance criteria, and a way to assess user impact.

  • Testing an AI-powered capability

    Hands-on LLM work and experience with GenAI systems are required. Suggested exercise: test a small LLM integration or RAG prototype against representative sample inputs; record incorrect outputs, a simple evaluation criterion, and what you would change before deployment.

  • Planning workflow automation

    The role identifies opportunities to automate team workflows using agentic AI and internal tooling. Suggested exercise: diagram a hypothetical analyst workflow, select one step to automate, and specify human review and failure handling. This is a preparation exercise, not a stated design requirement.

  • Using telemetry to make a decision

    Instrumenting products with analytics and telemetry is listed as a nice-to-have. Suggested exercise: define a few measures for a hypothetical detection feature and explain how their results would inform a product decision.

04

Your preparation checklist

  • Map your evidence to the role

    Prepare a one-page inventory of examples you can substantiate: product development, AI-powered applications, hands-on LLM and coding-tool work, and technical education or equivalent experience. Keep preferred security and infrastructure experience separate from stated requirements.

  • Build a concept-to-launch walkthrough

    Select a product you helped take from an ambiguous problem to launch. Document the user need, requirements, decisions, collaborators, shipped outcome, and evidence of user impact; be precise about your contribution.

  • Demonstrate a rapid prototype

    Prepare a short walkthrough of an AI feature you prototyped, if available: the hypothesis, what you built with tools such as Cursor or Copilot, how you tested feasibility, and what you changed or rejected.

  • Practice a security-product design exercise

    As interview preparation, sketch a hypothetical threat-detection or analysis capability. Specify users, inputs, an initial feature scope, evaluation measures, and trade-offs among security effectiveness, performance, usability, and operational complexity.

  • Explain how you would validate direction

    Draft a brief decision memo showing how internal data, threat intelligence, and market signals could inform an opportunity. Label assumptions, describe what evidence you would seek, and propose a measurable test rather than implying access to 7AI’s data.

  • Prepare an AI workflow and evaluation example

    If your work supports it, diagram an LLM integration, RAG pipeline, or agentic workflow, including failure modes and evaluation. Explain where human review would remain useful in a proposed team-automation use case.

  • Bring targeted questions for 7AI

    Prepare questions about how product, engineering, security research, and go-to-market teams collaborate; what evidence guides early validation; and how the team evaluates detection, response, or analysis capabilities before deployment.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

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

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