AI Engineer / Forward-Deployed AI Engineer LLMs, RAG, MCP, AI Agents & Applied AI

About Us We are a small, growing technology organization building AI-powered products, automation systems, and intelligent workflows. We are looking for a Forward-Deployed AI Engineer / AI Engineer who can work directly on real-world problems and turn them into practical AI solutions. The role involves working across LLMs, RAG, MCP, AI agents, tool calling, APIs, backend systems, automation, and AI integrations. This starts with a small paid technical engagement. If the collaboration goes well and the implementation meets our expectations, there is potential for a larger project and long-term contract. What We're Looking For We are looking for an engineer who can operate across the modern AI stack, including: LLM Engineering RAG (Retrieval-Augmented Generation) MCP (Model Context Protocol) AI Agents & Agentic Workflows Forward-Deployed AI Engineering AI Automation Tool Calling & Function Calling Vector Databases & Embeddings Knowledge Bases Prompt Engineering LLM APIs AI-powered APIs & Integrations Python / FastAPI Backend Engineering PostgreSQL / Supabase Git / GitHub Experience with technologies such as OpenAI, Anthropic, Claude, LangChain, LlamaIndex, MCP servers, vector databases, and agent frameworks is a strong advantage. You don't need to be an expert in every technology listed above. We are primarily looking for someone who understands how these components fit together and can build working systems. Forward-Deployed Engineering This role is not limited to writing isolated AI demos. We want someone who can: Understand a real business or technical problem Identify where AI can actually provide value Design an appropriate architecture Connect LLMs to external tools and data Build RAG and agentic workflows Develop APIs and backend services Integrate AI systems into existing workflows Debug and iterate quickly Communicate technical decisions clearly You should be comfortable moving from: "Here's the problem" → "Here's the architecture" → "Here's a working prototype." Initial Paid Technical Task The first engagement will be a small paid technical task designed to evaluate practical engineering ability. The task may involve building or improving a small system involving: An LLM RAG MCP / Tool Integration External APIs A Vector Database An AI Agent or Workflow Python / FastAPI Backend Structured Outputs The exact task will be provided to the selected candidate. The objective is not to build a massive production system during the initial engagement. We want to understand how you: Approach problems Design systems Write code Integrate AI components Deliver a working solution What We Will Evaluate We will evaluate: Practical AI engineering ability LLM architecture understanding RAG implementation MCP and tool integration Agentic workflow design Backend/API engineering Code quality System design Problem-solving Ability to work independently Communication Documentation Ability to move quickly from prototype to working implementation We care more about building real systems than simply knowing AI terminology. Requirements Strong practical experience with several of the following: Python LLM APIs RAG Vector Databases Embeddings MCP AI Agents Tool / Function Calling FastAPI REST APIs PostgreSQL Git / GitHub Nice to Have Experience with: OpenAI APIs Anthropic APIs Claude LangChain LlamaIndex MCP Servers Agent Frameworks Supabase Redis Docker Cloud Deployment AI Automation Production AI Applications Building AI Products from Scratch Engagement This is a paid initial technical engagement. We intentionally start with a small project so both sides can evaluate the technical fit and working relationship. Successful completion can lead to: A larger project A long-term contract Increased technical ownership There is no artificial limitation on the potential scope of the larger engagement—we are looking for someone who can grow with the project. How to Apply Please provide: A short introduction Your experience as an AI / Forward-Deployed AI Engineer 1–3 relevant projects you have personally built Your experience with LLMs, RAG, MCP, and AI Agents GitHub and/or Portfolio links A brief explanation of how you would approach a small AI engineering task Please focus on real implementations and things you have actually built, rather than generic descriptions of AI technologies. We are particularly interested in engineers who can take an ambiguous problem and turn it into a working, maintainable AI system.