iSpeedToLead - Senior AI Product Engineer

• 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. • This is not for you if • You want a prompt-only role or a research position focused on training foundation models. • You do no-code-only automation and can't own real production code. • You call yourself a product manager who delegates the build. • You ship unreviewed "vibe coding" and call it done. • You need a complete spec before you can start. • You optimize for demo quality instead of measurable business impact. • What we expect from you (first 3 months) • We don't do long onboarding: access on day one, context in days, first shipped improvement in the first 30. • Days 1–30 — Understand and ship: you have learned the product, data, and workflows, identified the best first problem with evidence, and released one useful, low-risk improvement in production. • Days 31–60 — Own the workflow: you have launched one meaningful AI product or automation with evaluation, monitoring, clear ownership, and a human fallback — running against real marketplace volume. • Days 61–90 (the bar) — Prove and repeat: the system shows measurable business impact (fewer manual hours, faster verification, lower refunds, better matching, higher conversion, or shorter time from idea to production), reliability is proven, and the solution is turned into a reusable pattern for future workflows or niches.