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.

Qualifications

8+ years of relevant experience, with at least 3 years in product management or a client-facing/ forward-deployed role Track record of taking multiple AI or software products from pilot to scaled production, ideally across more than one client 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 Experience working with a cross-functional product team on a significant product area: crafting product vision and strategy, defining product requirements, coordinating resources from other groups (marketing, legal, etc.), and driving the team to achieve key milestones and goals 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 Professional fluency in Portuguese and English Willing to travel to client sites in-region 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