Product Manager, Enterprise Solutions (Forward Deployed)

Enterprise Solutions is Meta's forward-deployed engineering team, deploying Meta Business Agents directly at enterprise customers. Our LATAM pods embed small teams (a PM plus engineers) with clients to design, build, and launch AI agents on WhatsApp, then turn what we learn into reusable product and platform. As a forward-deployed Product Manager, you will own enterprise customer deployments end to end, working on-site with clients across Latin America.

Product Manager, Enterprise Solutions (Forward Deployed) Responsibilities:

Be responsible for leading and driving a complex product area

defining success, prioritizing problems and identifying the best strategies, considering wider organizational and company context.

Adapt and adjust your strategies to reflect learnings and changes in context.

Embed with enterprise clients as the product owner of their Meta Business Agent deployment, from first use-case scoping through launch and expansion.

Own a customer engagement end to end: define the use case and success metrics with the client, design the agent solution, and drive it to production with your pod (SWE, DE, and BE or PE partners).

Hill-climb agent quality in the client’s environment: build and run evals, diagnose failures, and iterate the agent to hit task-completion and quality targets.

Run two to three client engagements in parallel, moving fast and rotating between customers as priorities shift.

Partner with Sales and Business or Partner Engineering to shape scope before anything is signed, and hand back to steady-state after launch.

Turn bespoke client learnings into reusable product and platform feedback for the Business Agent product and shared tooling.

Travel to client sites in-region for discovery and co-build immersions.

Critically evaluate when AI is (and isn’t) the optimal solution for a client’s problem, with sophisticated articulation of tradeoffs, risks, and second-order effects.

Demonstrate deep understanding of system and architecture trade-offs and how they impact client outcomes

lead credible technical discussions with engineering partners.

Use AI-native practices (evals as a first-class discipline, data strategy, building with AI tools) to accelerate deployment and quality.

Communicate progress, risks, and outcomes clearly to client executives and internal leadership.

Support the growth of other PMs and cross-functional team members by providing mentorship and coaching on AI-native practices.

Minimum Qualifications:

8+ years of relevant experience, with at least 3 years in product management or a client-facing/ forward-deployed role

Bachelor’s degree (or relevant degree equivalent): STEM subject ideal but not essential (Computer Science, Engineering, Information Systems, Analytics, Mathematics, Physics, Applied Sciences)

Experience owning a customer or engagement end to end and working hands-on with engineers to deliver

Demonstrated experience using AI-enabled tools to build product artifacts, and developing AI-native strategies including evals and data strategies

Demonstrated experience analyzing large-scale, complex data sets and making effective decisions based on data

Demonstrated experience in communication, bringing extreme clarity to complex and technical messages at the appropriate level for the audience

Willing to travel to client sites in-region

Preferred Qualifications:

Consulting or forward-deployed background (e.g., Palantir-style embedded delivery, systems integration, or solutions engineering)

Managing technical relationships with client executives or strategic partners

Abstracting bespoke enterprise feedback into reusable product features

Familiarity with enterprise data infrastructure, connectors, and how well-structured data enables AI outcomes

Experience with AI-native strategies including evals and data strategy

Track record of managing Fortune 500 C-suite relationships and building or codifying a repeatable deployment playbook that other pods reuse

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

About Meta:

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today - beyond the constraints of screens, the limits of distance, and even the rules of physics.

Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.