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Senior AI Product Engineer

Ispeedtolead
CompanyIspeedtolead
CategoryEngineering
LocationKyiv
RemoteRemote
EmploymentNot stated
LevelSenior
SalaryNot stated by the employer
Posted18 Jul 2026
Last verified5 Aug 2026
SourceEmployer ATS (ashby)
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Description
ABOUT THE COMPANY 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. WHAT YOU'LL BE DOING: - 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 r