Product Manager, AI Agents & MCP Tools

Front Door Defense; Boston, Northern (MA, KY); Hybrid; USD 129,000 - 194,000; Full time

Benefits offered by this job

Equity

Medical insurance

401K

Job summary

Recorded Future is seeking a hands-on Product Manager for AI Agents & MCP Tools in Boston. You will own the lifecycle of AI agents, orchestrate LLMs and MCP tools, and work with engineers in GitHub to ship impactful capabilities.

This mid-level role emphasizes building practical solutions, evaluating models, and delivering tangible customer value in a fast-paced environment.

Qualifications

• Hands-on PM with lifecycle ownership of AI agents and MCP tools.
• Strong product judgment on what to ship.
• Technical fluency and comfort in GitHub and prompt engineering.
• 3–5 years in product management or related role.

Responsibilities

• Own the end-to-end lifecycle of AI agents — design, build, evaluation, customer validation, deployment.
• Build agents that orchestrate LLMs and tools against real-use cases, selecting models based on tradeoffs.
• Refine MCP tool surface, write clear tool descriptions, and improve coverage.
• Analyze usage patterns to identify gaps and opportunities for new tools.
• Define and track performance metrics like accuracy, latency, and customer satisfaction.

Skills

Agent Builder

Model-Literate

Tool Design

PM Skills

Tools

GitHub

MCP Framework

Prompt Engineering

Job description

Product Manager, AI Agents & MCP Tools

Own the end-to-end development and deployment of AI agents and MCP tools

Location: Boston, Massachusetts

Compensation: $129,000 - 193,500 USD / year

About The Role

Product Manager, AI Agents & MCP Tools

With 1,000+ intelligence professionals serving over 1,900 clients worldwide, Recorded Future is the world's most advanced, and largest, intelligence company!

We're looking for a hands-on Product Manager to own the lifecycle of Recorded Future's AI agents and MCP tools — the intelligent workflows and tool integrations that apply large language models to real threat intelligence problems for our customers. This is not a role focused on training models or building LLMs. Instead, you'll build, evaluate, and ship agents that orchestrate existing models, and shape the MCP tools those agents and our customers rely on to access Recorded Future intelligence.

You'll be responsible for the full arc of an agent — from initial build, through evaluation and iteration, to customer testing and deployment — as well as the quality and coverage of our MCP tool surface. You'll write and maintain evals, refine tool descriptions, identify gaps in tool coverage, analyze how tools are actually used, and make the call on what ships. This role suits someone who is technically fluent, comfortable getting their hands dirty in GitHub and prompt engineering, and grounded in strong product judgment about what's worth building.

This is a mid-level role for a builder who moves fast, tests rigorously, and cares more about whether an agent or tool solves the customer's problem than whether it demos well.

What You'll Do:

• Own the end-to-end lifecycle of AI agents — design, build, evaluation, customer validation, and deployment.
• Build agents that orchestrate LLMs and tools against real intelligence use cases, selecting the right model for each task based on capability, latency, and cost tradeoffs.
• Own and refine the MCP tool surface — writing clear, effective tool descriptions, identifying gaps in coverage, and improving how tools expose Recorded Future intelligence to agents and customers.
• Analyze MCP tool usage patterns to understand what customers and agents actually invoke, where tools fail or underperform, and where new tools are needed.
• Write, maintain, and expand evaluation suites to measure agent and tool quality, catch regressions, and guide iteration; update agents and tools as models, data, and customer needs evolve.
• Test agents and tools directly with customers, gathering feedback and confirming that outputs meet their workflows and expectations before and after launch.
• Work hands-on in the codebase (GitHub) alongside engineers — reviewing changes, prototyping, and contributing to agent logic and tool definitions where appropriate.
• Maintain a working understanding of the LLM landscape, tracking the strengths, weaknesses, and cost profiles of available models to make informed build decisions.
• Define and track agent and tool performance metrics — accuracy, task completion, tool invocation success, latency, cost per task, and customer satisfaction.
• Prioritize the agent and tool roadmap, focusing effort on the capabilities that deliver the most customer value.
• Partner with intelligence, engineering, and design teams to ensure agents and tools integrate cleanly into the broader platform and customer experience.
• Establish repeatable practices for building, evaluating, and shipping agents and tools reliably and safely.

What You'll Bring:

• Agent Builder with hands-on experience building LLM-powered agents or workflows — or the technical aptitude to ramp up quickly.
• Model-Literate, with working knowledge of major LLMs and a practical sense of their pros, cons, and cost tradeoffs.
• Tool-Design Sense, able to write clear tool descriptions, reason about how agents select and invoke tools, and spot coverage gaps. Familiarity with MCP (Model Context Protocol) or similar tool-integration frameworks is a plus.
• Technically Comfortable, able to work in GitHub, read and reason about code, and engage credibly with engineers on agent and tool design and evaluation.
• Evaluation-Minded, understanding how to define quality, write evals, and use them to drive iteration rather than relying on vibes.
• Data-Informed, comfortable analyzing usage patterns to guide decisions about what to refine, build, or retire.
• Customer-Oriented, skilled at working directly with users to validate that what's built actually solves their problem.
• Strong PM or Business Analyst Skills, able to prioritize, define requirements, and connect technical work to business outcomes.
• Pragmatic and Outcome-Driven, comfortable shipping, measuring, and improving in fast iteration cycles.
• Cybersecurity experience is a plus but not required — provided you can ramp up quickly on the domain.
• 3–5 years in product management, technical program management, or a hands-on technical role building AI/LLM-powered products, agents, or tool integrations

The base salary range for this full-time position is $129,000 - $193,500. Our salary ranges are determined by role, level, and location. The salary displayed reflects the range for new hire salaries for the position across all US locations. Within the range, individual pay is determined by state, work location and additional factors, including job-related skills, experience, and relevant education or training. This position may be eligible for incentive compensation, equity, and medical, dental, vision, life insurance and 401K. Your recruiter can share more about the specific details of the compensation and benefit package during the hiring process.