AI Solutions Engineer

We are seeking an exceptional problem solver who operates at the intersection of AI infrastructure, enterprise systems, and production‑grade automation. The ideal candidate owns the full lifecycle of an engagement: audit the data, architect the solution, build it, deploy it, and prove the ROI. This role requires working inside mid‑market and enterprise companies, diagnosing which workflows are eligible for automation, designing the agent architecture, and connecting AI directly to source systems. You ship systems that run without human input. You are not writing tickets someone else scoped. You are the person in the room figuring out where the leverage is. What You'll Do Build Cogs, not Grease. You'll design and deploy infrastructure that replaces entire workflows autonomously, not tools that make employees 10% faster with ChatGPT. Break the Linear Ceiling. Most companies scale by adding headcount. You'll decompose complex workflows into atomic units of work, identify which units can be handled by agents, and remove the human bottleneck from the process entirely. Stitch fragmented data. Client data lives in six different places and none of them talk to each other. You'll build the pipelines and integrations that connect these sources so AI can reason across the full picture. Architect using vendor‑neutral, future‑proof standards (MCP) so clients aren't locked into any single model provider. Ship production systems. If it doesn't run without a human babysitting it, it's not done. Work directly with C‑suite stakeholders to translate technical decisions into P&L impact. You Should Have 5+ years building production software, with at least 2 years working with LLMs or agent frameworks in production. Deep experience with Python, TypeScript, or Go, and strong opinions about when to use which. Hands‑on work with cloud platforms (GCP or AWS), containerization, and CI/CD pipelines. Experience building data pipelines and integrations across messy enterprise systems (ERP, CRM, legacy databases). Comfort working directly with non‑technical executives. You can explain what you're building and why it matters to the business. A bias toward shipping. You'd rather deploy something that works Monday than architect something perfect by Q3. Your Candidacy Is Stronger If You've built AI systems at a FAANG‑tier company (Google, Meta, Amazon) and want to apply that at a smaller scale where you own the outcome end‑to‑end. You've worked in consulting or client‑facing engineering and know how to navigate ambiguity and stakeholders who don't speak your language. You've founded or been early at a startup. You understand constraints and building under pressure. You have experience in manufacturing, logistics, healthcare, or professional services, the industries where our clients operate. This Probably Isn't For You If You want clearly scoped Jira tickets handed to you each sprint. You're primarily interested in research or model training. We build applied systems, not papers. You’d rather optimize a model’s benchmark score than figure out why the warehouse manager won't use the system you built. You think "the client should just fix their data" is an acceptable answer. About Us Our team is former tech CEOs, exited founders, and AI engineers from Google, Meta, and Coinbase. We've built AI systems serving billions of users at large tech companies and scaled our own startups from zero. We understand constraints, P&Ls, and what it takes to make AI work inside companies that weren't born digital. You’ll work on real problems at real companies. Every system you build has a measurable dollar impact. Compensation Competitive base + performance‑based comp tied to client outcomes. #J-18808-Ljbffr Your AI Department