Senior AI Product Engineer

iSpeedToLead

12h•Remote

About The Position

Turning marketing into a market-domination weapon, we created a new category — the marketplace for future customers. Businesses shop for leads as they shop for products in e-commerce: you see everything about a lead before you buy — full transparency, control, and predictable outcomes. We're the #1 lead marketplace in the USA, and we're accelerating. Role overview We're looking for a Senior AI Product Engineer to build the AI operating layer behind our marketplace. You take a valuable but unclear business problem — improve lead quality, cut refunds, automate a manual workflow, extract intelligence from calls — and turn it into a dependable production system end-to-end: investigate, define success, design, build, deploy, and improve on real usage data. You are a one-person AI product team for high-leverage problems, not a spec-taker. We are expanding beyond real estate into new service-business niches, and growth is powered by reusable software and automation — not proportional growth in manual work. Every system you ship either compounds into that leverage or exposes where it leaks — no in-between. Why this role exists Our growth is not limited by ideas or data — we already run 40K+ members, 750+ verified daily leads, and 50B data points powering AI. It's limited by how fast we can turn ambiguous, high-value problems into shipped AI systems that actually move business metrics. We need a builder who owns the complete production outcome — not a prompt tinkerer, not a researcher, not a PM who delegates the build.

Requirements

Strong record of building and shipping production software end-to-end, ideally in a SaaS, marketplace, or workflow product — with real users and real revenue on the line.
Strong Python and/or TypeScript, plus solid backend fundamentals: APIs, databases, auth, data models, testing, and deployment.
Hands-on experience shipping LLM-powered functionality using model APIs, structured outputs, tool/function calling, retrieval, context engineering, and agentic workflows.
Production automation experience: webhooks, events, queues, schedules, retries, idempotency, monitoring, and failure recovery — not just happy-path demos.
Practical evaluation skills: test sets, traces, quality metrics, regression checks, feedback loops, and root-causing real production failures.
Daily fluency with AI coding agents (Claude Code, Codex, Cursor, or equivalent) — you review, test, refactor, and secure generated work, not ship it raw.
English C1+; our stakeholders, users, and business owners are US-based, and communication must be sharp and clear.
AI-first by default — if you are not already using AI to 3x your own output, you will be behind the rest of engineering.
Product ownership and strong judgment in ambiguous, fast-moving environments — you clarify goals, make tradeoffs, and connect every technical decision to a business KPI.

Nice To Haves

Voice AI, call transcription, telephony, SMS, email, CRM, or customer-support automation experience.
Lead generation, marketplaces, real estate, sales technology, or marketing automation background.
Ranking, scoring, prediction, experimentation, or data pipeline experience.
Cloud deployment, Docker, CI/CD, observability, security, and handling sensitive customer data.
MCP, vector search, LangGraph or equivalent orchestration, Temporal, n8n, or custom workflow engines.
Track record taking a product or automation from zero to one in a fast-moving startup.

Responsibilities

Discover high-value product and operational problems by working directly with users, business owners, and internal teams, then frame clear success criteria before writing code.
Design and ship customer-facing AI features and internal automations across lead quality, matching, prediction, call intelligence, CRM, support, and operations.
Build agents and workflows that safely use tools, APIs, databases, calls, SMS, email, and other company systems — with human approval where risk requires it.
Create the context layer around models: instructions, retrieval, memory, structured outputs, tool definitions, permissions, and feedback loops.
Build reliable event-driven and scheduled automation using webhooks, queues, schedulers, retries, idempotency, audit logs, and graceful fallbacks.
Create evaluation datasets and automated checks that measure usefulness, accuracy, failure modes, regressions, latency, and cost — before and after release.
Investigate real production failures and improve the complete system, not just the prompt or the model response.
Turn successful solutions into reusable components and patterns that support additional iSpeedToLead niches without rebuilding from zero.
Own architecture, quality, security, and business outcomes — not just the pull request.