AI / Applied AI Engineer

3 days ago
Phoenix AZ, Maricopa County, AZ; Arizona, United States Ignitium: ABX Orchestration Full-time
Considering applying for this job Do not delay, scroll down and make your application as soon as possible to avoid missing out. We're looking for a hands-on Applied AI Engineer to build the AI tools, agents, and automations that make Ignitium faster, and increasingly, to build custom AI solutions and PoCs for our clients. Our work spans dozens of interconnected systems: workflow platforms (n8n, Clay, Zapier, Make), data and BI tools (BQ, CloudSQL, Domo, Google Sheets), project management (Asana), enrichment and outbound tools (Outreach, , Zoominfo, SalesLoft, Sales Navigator), intent data (Bombora, 6sense, Demandbase), ad platforms (LinkedIn, Meta, Google, Reddit, X), and a growing AI layer of Agents, MCP servers, custom skills, and browser automation. AI-assisted development: We expect you to use AI coding tools aggressively. AI assistance multiplies engineering skill, it doesn't replace it. You must be able to read, debug, and own what you ship. Internal AI Tools & Automations (core of the role) Design, build, and maintain AI-powered tools, agents, and workflows used daily by internal teams. Harden what you build: error handling, logging, monitoring, and graceful failure for unattended workflows. Build custom AI solutions, demos, and PoCs for client needs, adapting internal patterns to client data and systems. Prototype rapidly, evaluate against real data, and iterate. Document PoCs and support hand-off into productionized implementations. AI Platform & Best Practices Apply best practices for context design, model selection, evaluation, guardrails, and data handling. Explain trade-offs (cost, latency, accuracy, reliability) in plain language. Strong coding competency in TypeScript/JavaScript and/or Python . You can build, debug, and maintain real software without an AI assistant, even though you'll rarely work without one. Git, testing and debugging discipline, and an instinct for when a quick script is fine versus when something needs to be built properly. Proven ability to take a vague request and ship a working solution end-to-end. Applied AI Experience Hands-on experience building production (or production-like) applications with AI integration. Working knowledge of prompt and context engineering, RAG, structured outputs, tool use, and agent architectures. Experience with a workflow automation platform (n8n preferred; Zapier or Make also valued), including writing custom code inside it. You'd rather ship a v1 this week than design a perfect system next month. Building or consuming MCP servers, or working with the Claude Agent SDK or agentic frameworks. Vector databases, search systems, or LLM evaluation frameworks. Background in B2B SaaS, GTM operations, RevOps, ABM/ABX, or agency environments. Multiple internal AI tools shipped, actively used, and trusted, with stakeholders able to point to hours saved or quality gains. Client-facing PoCs and custom builds that helped win, retain, or expand client relationships. Automations that keep working: they fail loudly, recover gracefully, and don't need babysitting.

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