Mechro Founding Applied AI Engineer

Gitwit•Tulsa, OK

2d•$165,000 - $190,000•Hybrid

About The Position

Build the AI Troubleshooting Engine That Powers the Future of Heavy Machinery. We’re not hiring just another engineer. We’re hiring the founding, forward-deployed engineer who will bring Mechro’s AI troubleshooting platform to life by owning customer outcomes end-to-end, from first discovery workshop to production rollout. You’ll work directly with machinery manufacturers (and their distributors/service teams) to turn messy, real-world troubleshooting into a continuously improving AI system. The technical partner who owns the zero-to-one build, shapes the product with real customers, and helps define the intelligence layer for the entire heavy-equipment ecosystem. Mechro is a new AI-native venture built inside Gitwit’s venture studio, designed in partnership with leading machinery manufacturers. Our mission is bold and simple: Turn frontline troubleshooting into a continuous learning system that makes machines smarter, support teams faster, and field operators more confident. This role is for the engineer who wants to build something that actually gets used on job sites, in service bays, and in the hands of technicians diagnosing real machines every day. Every machine manufacturer is dealing with the same pain: Support lines overwhelmed with repetitive questions Tribal knowledge disappearing as senior techs retire Field intelligence scattered across notebooks, calls, and emails Manuals that can’t keep up with the real world Mechro changes that. We’re building an AI troubleshooting assistant that becomes the first line of diagnosis for distributors, support techs, and field operators. Not a chatbot bolted onto a PDF, but a full-stack system that ingests manuals, logs, historical service data, field notes, and escalations to create a living, continually improving knowledge base. Every interaction makes the next one better. Every fix makes the manufacturer smarter. This is the future of support for heavy machinery, and you’ll be the one building it. Your Impact as the Founding Applied AI Engineer This is a delivery-first engineering role that will position you as the technical cornerstone of Mechro. You’ll be embedded with customers to understand workflows, define success, integrate their systems/data, and ship working solutions into production. Then you’ll bring what you learn back into the core platform so each deployment makes the product stronger and more repeatable. This role is perfect for someone who Thrives in zero-to-one environments Ships fast and cares deeply about product usefulness Wants to build with real users and see immediate impact Enjoys owning AI systems end-to-end Likes wearing multiple hats and setting the tone for future engineers 50–70% forward-deployed delivery Discovery, integration, implementation, go-live, iteration 30–50% platform/product engineering Core architecture, reliability, AI evaluation + monitoring Your time focus will flex with customer onboarding cycles.

Requirements

5+ years full-stack engineering experience with shipped, user-facing products
Strong experience with React + TypeScript, Node/NestJS, and SQL/NoSQL databases
Practical experience integrating LLMs/AI APIs into real applications
Zero-to-one ownership stories that show grit, ambiguity navigation, and user impact
Experience running B2B/enterprise customer integrations
Comfort balancing speed with reliability in early-stage systems
Excellent written and verbal communication
Strong “delivery energy”. You like owning outcomes in the real world, not just writing elegant code.

Nice To Haves

Experience with RAG systems, vector databases, or ML evaluation pipelines
Familiarity with SOC2-style security practices
Prior startup or founding engineer experience
Strong product sense and the ability to simplify complex workflows

Responsibilities

You’ll own the architecture, help drive decisions about what gets built and why.
Lead full-stack development using React + TypeScript, Node/NestJS, and Postgres/MongoDB
Build the AI layer LLM integrations, retrieval, embeddings, evaluation loops
Stabilize and scale the MVP into a production-ready troubleshooting assistant
Run technical customer integrations, including scoping, implementation calls, and on-site visits
Implement reliability systems monitoring, alerting, runbooks, incident response
Establish early engineering culture, onboarding docs, architecture patterns, and delivery cadence
Translate user problems into product decisions that shape the roadmap