Forward Deployed Product Manager

Bolo AI•Palo Alto, CA

4d•Hybrid

About The Position

Bolo AI is seeking a Forward Deployed Product Manager to own customer engagement outcomes from problem discovery through shipped impact. This role is situated at the intersection of customer, GTM, and engineering, focusing on understanding operational workflows, scoping MVPs, translating customer needs into product requirements, and driving engagements to measurable business value. The core skill is turning ambiguity into shipped outcomes, working with customers who have complex workflows, fragmented data, security constraints, and high expectations. The goal is to create clarity, sequence work, manage tradeoffs, and ensure that delivered solutions are both useful for the customer and reusable for Bolo. The role is accountable for customer success and product learning, identifying repeatable patterns, shaping product direction, and building scalable delivery processes. This is an in-person, customer-facing role that can be based in Palo Alto, CA or Houston, TX, requiring regular team collaboration, customer meetings, on-site discovery, and travel up to 50% during intensive periods.

Requirements

6–8+ years of experience in a high-ownership, customer-facing role such as consulting, forward-deployed product, enterprise software implementation, solutions, technical delivery, product management, or business operations.
A track record of running complex client-facing work under ambiguity, including scoping problems, aligning stakeholders, managing tradeoffs, and driving to a concrete outcome.
Strong commercial judgment. You understand that customer success, expansion, renewal, and product direction are connected.
Strong technical fluency. You should be comfortable reasoning through data, integrations, AI workflows, implementation constraints, and tradeoffs with engineers.
Clear product judgment. Direct PM experience is not required, but you should be able to define MVP scope, write clear requirements, make prioritization tradeoffs, and distinguish customer-specific requests from reusable product patterns.
Excellent communication with executives, operators, engineers, and data teams. You can push back without becoming defensive or overly accommodating.
Evidence that you can operate in a very early-stage startup environment: low structure, high ambiguity, fast context switching, and no perfect playbook.
Real curiosity about AI. You actively experiment with AI tools and can think critically about where they work, where they fail, and what it takes to make them reliable in production.

Nice To Haves

Exposure to energy, oil and gas, heavy industry, applied AI/ML, or LLM-based products is a strong plus.

Responsibilities

Own customer engagements from discovery through deployment, adoption, and measurable impact.
Build credibility with customer stakeholders across field teams, technical teams, executives, and commercial buyers.
Immerse yourself in the customer’s world through both their data and their day-to-day workflows.
Translate messy customer problems into clear problem statements, MVP scope, data requirements, user stories, and product requirements.
Partner with engineering to turn customer workflows and datasets into stable, extensible AI-powered solutions.
Manage tradeoffs across customer urgency, technical complexity, commercial value, and long-term product reuse.
Scope phased deployments that create value quickly without creating unsustainable custom work.
Lead customer rollout and adoption so shipped solutions become part of real workflows.
Stay close to expansion, renewal, and commercial outcomes, not just delivery milestones.
Identify repeatable patterns across engagements and turn them into product and delivery processes as Bolo scales.