AI Product Manager

About the role

This role is responsible for conceiving and delivering internal products and platform solutions that serve critical business needs across all defense domains. The work directly supports defense companies in achieving their high-stakes missions through purpose-built data and AI capabilities. You will own the end-to-end lifecycle of specific products, from initial discovery and requirements gathering through delivery, validation, and ongoing optimization. A core responsibility is to translate complex stakeholder needs into a clear product vision and a pragmatic roadmap that guides development and delivery. You will facilitate close collaboration with technical and operational teams to ensure solutions are feasible, valuable, and aligned with defense compliance standards. Another key area of ownership involves defining and tracking product metrics that quantify the impact and return on investment of the solutions you ship. You will also synthesize feedback from users and stakeholders to continuously validate solutions, manage expectations, and refine the product over time. Ultimately, this role ensures that data-driven products are not just built, but are actively used to drive measurable outcomes for defense-focused organizations.

Key facts

What you'll do

Define internal products and platform solutions that address business needs across defense and mission critical domains.

Create and maintain product roadmaps that outline the vision, direction, and timeline for development and delivery.

Engage stakeholders continuously through feedback collection, solution validation, priority alignment, and expectation management.

Break down high level objectives into concrete development tasks and assign work to the team to ensure timely execution.

Define product metrics and key performance indicators to measure the value, impact, and adoption of new solutions.

Present results and insights to stakeholders using clear narratives backed by data and evidence.

Leverage a deep understanding of modern AI capabilities and limitations to select the most appropriate technology approach for each problem.

Differentiate between methods such as RAG and Fine-tuning to apply the right technical strategy for the use case.

Recognize AI risks and related problems early to manage tradeoffs, constraints, and compliance considerations inherent in defense work.

Coordinate input from diverse stakeholders and facilitate alignment to move projects forward efficiently.

Requirements

Four to seven years of product management experience is required for this role.

Experience launching products from scratch is necessary to navigate early stage decisions and ambiguity.

Deep understanding of the capabilities and limitations of modern AI technologies is required.

You must understand the differences between RAG and Fine-tuning to apply the correct method.

You must recognize AI risks and problems to effectively manage tradeoffs and constraints.

Strong facilitation skills are needed to coordinate input and align stakeholders with differing priorities.

You must make data driven decisions using analytical reasoning and evidence.

A strong portfolio of past analyses is valued more than advanced degrees in many hiring decisions.

Nice to have

Experience in defense or mission critical environments is valued.

Familiarity with military workflows and compliance standards is preferred.

Practical notes

This role is based on a remote contract.

Employment is formal and complies with labor regulations.

Military personnel may receive specific leave and documentation support when applicable.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study.

Candidates may be asked to design a metric, interpret an experiment, or build a small model.

Some companies give a take-home analysis.

Expect questions about past projects and the business impact of your work.

Interviewers often evaluate how you communicate uncertainty and business impact, not only the math.

Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Work in this field focuses on defense related problems and impact.

Modern AI methods such as large language models influence how products are designed.

Data analysis guides decisions throughout the product lifecycle.

Cross functional collaboration is common in product teams.

Clear communication aligns stakeholders and defines success metrics.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year.

Asking what past hires did well is a strong final question.

Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead.

Many professionals specialize in machine learning, analytics, or infrastructure.

Cross-functional work with product and engineering teams becomes more important at senior levels.

The field changes quickly, so continuous learning is part of the job.

Professionals who can translate numbers into decisions tend to advance fastest.