Senior Product Manager

Job Overview

About Raynmaker:

Raynmaker is redefining sales for small and medium-sized businesses with an enterprise-grade, end-to-end autonomous platform. Powered by our proprietary Rayn Brain™ sales insight engine, Raynmaker converts leads into customers by handling every step of the sales journey, from first contact to closed deal, with an AI agent that books appointments, takes payments, and answers questions 24/7, all in a lifelike, brand-aware, context-sensitive human voice.

For SMBs, it’s like hiring a world-class sales team on demand.

About The Role: You want to build at the frontier of AI, where agents don’t just augment humans, they replace entire workflows for Small and Medium businesses.

You want to work directly with our founders, our engineers, and real customers to push the boundaries of what autonomous voice agents can actually do.

You want to be part of a company where your fingerprints are on the product, the strategy, and the industry we’re redefining.

You want to build real AI in the real world, where your work ships quickly, learns continuously, and creates measurable revenue every day.

Why You? You’re a product thinker with a technical backbone—comfortable bridging the gap between engineering complexity and real-world user needs. You’ve shipped features in agile environments, written clear specs, worked directly with engineers and QA, and know how to prioritize for speed without sacrificing impact.

You thrive in a dynamic environment and move quickly from idea to execution. Whether it’s refining a multi-step AI workflow or identifying a fast path to unblock users, you break down complexity into action. You care about outcomes, not just output—and you drive measurable improvements through experimentation, iteration, and smart defaults. You have a foundational understanding of how software is built and are excited by the challenge of translating emerging AI technologies into simple, effective product experiences.

You want to build tools that matter, collaborate tightly with high-agency teams, and own the full lifecycle from prototype to production.

• Build voice-AI powered workflows that automate full-funnel sales and service operations (inbound call flows, qualification, pricing logic, sentiment-based branching, scheduling, payments).
• Collaborate with engineering to design agent architectures blending:
  • LLM reasoning
  • RAG pipelines
  • Structured or rule-based logic
  • Guardrails and evaluator loops
• Own prompt flows end-to-end
  : conversation design, edge‑case handling, fallback logic, experiment configurations, and continuous tuning.

• Write crisp, implementable PRDs with tight MVPs and explicit logic (no ambiguity, no fluff).
• Define success metrics aligned to operational performance:
  • Conversion rate
  • Handle time
  • Zip code accuracy
  • Payment completion
  • Deflection to scheduling
  • Agent containment
• Translate field learnings into spec revisions with rapid iteration cycles.

AI Systems Thinking

• Turn complex concepts (embeddings, vector DBs, retrieval strategies, agentic chaining) into clear product decisions
  .
• Work hands‑on with:
  • Call recordings
  • Transcripts
  • Supervisor‑agent outputs
  • RAG logs to diagnose failures, identify gaps, and guide engineering solutions.

Fast, Iterative Build Cycle

• Identify and triage edge cases quickly based on business and user impact.
• Run tight feedback loops using real customer calls, tuning cycles, and human‑in‑the‑loop labeling.
• Move prototypes into production with clean documentation and clear engineering handoffs.

Cross‑Functional Leadership (Without Bureaucracy)

• Partner directly with engineering, implementation, sales, and leadership to bring clarity and direction.
• Drive alignment around product rationale across technical and non‑technical stakeholders.
• Bring decisiveness where there is ambiguity and momentum where there is drift.

Lifecycle Ownership

• Support internal tools, configuration dashboards, QA harnesses, and other operational systems.
• Own external‑facing surfaces such as reporting dashboards and performance insights.
• Take features from whiteboard → prototype → production rollout → post‑launch optimization
  .

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