Internal AI & Automation Engineer

About The Company

Lithosquare is an Earth Exploration company finding the critic materials required for our energy transition. Built around its AI and software platform, Lithosquare radically improves the efficiency of finding new deposits while minimizing the impact of mineral exploration. Based in Paris, Lithosquare team gathers experts of geosciences and AI delivering our product in a fast-paced environment.

About The Role

We are seeking our first Applied AI & Automation Engineer to scale our technical and operational capabilities across the entire company. This is a transversal role: you will sit at the intersection of Ops, Tech & Product, Field Operations, and every other teams to identify operational bottlenecks and solve them with intelligent systems.

You won’t just be "automating tasks"; you will be architecting stateful AI workflows and deploying autonomous agents that handle complex, multi-step logic. You will bridge the gap between "no-code" agility and "hard" software engineering to build a truly AI-native organization.

Core Responsibilities

• Transversal Solution Architecture: Partner with teammates across Geosciences, Sales, and Ops to map their workflows and design end-to-end AI systems that solve their specific "pains."
• Hybrid Automation & Agentic Systems: Build robust pipelines using a mix of n8n for orchestration, Python for custom logic, and Agentic frameworks (like OpenClaw) for reasoning.
• Full-Stack Prototyping: Own the full lifecycle—from identifying an opportunity to shipping a production-ready internal tool (e.g., an automated market intelligence engine or a seamless auto-documentation agent).
• Extending AI Capabilities: Develop and maintain MCP (Model Context Protocol) servers and API integrations to give our agents secure access to internal data and external geological tools.
• AI Observability & Iteration: Implement feedback loops to track the performance and reliability of your automations, moving from "vague prompts" to deterministic, high-quality outputs.
• Culture & Enablement: Seed an AI-native culture by coaching internal "AI champions," running engaging enablement sessions, and embedding AI practices (Cursor, Notion AI, custom agents) into team norms.

Technical Profile & Requirements

• Experience: 0–2 years of professional or personal project experience building, shipping, and transforming workflows with applied AI or automation.
• The Builder Mindset: You are an "AI-native" engineer who prefers shipping working PoCs over writing specs. You have a portfolio of personal projects or early professional experience showing you can build "end-to-end."
• The current stack:
• Orchestration: Experience with n8n, LangGraph, or similar workflow engines.
• Languages: Strong proficiency in Python (for data processing and agent logic).
• LLM Engineering: Deep understanding of agentic stacks including notion like RAG, tool-calling, Prompt Engineering, and MCPs.
• Integrations: Comfortable working with APIs, Webhooks, and various data formats (JSON, Markdown, SQL).
• Transversal Communication: You can translate a "business pain" into a "technical schema" and explain your architectural choices to non-technical peers.
• Adaptability: You thrive in ambiguity and are excited by the prospect of touching every part of a growing deep-tech startup.