Overview

Full-time freelance · 2 months · Extendable month to month

Qualifications

• Full-stack apps you have shipped with an LLM at the core. Send us links or repositories. We want to see something that does more than wrap a chat box around an API call.
• Hands-on LangGraph experience, in Python or JavaScript. You can explain how state moves through your graph, what happens when a node fails halfway through, and how you resume a run.
• Solid frontend engineering. React (including Next.js) with TypeScript, or Flutter, including streaming interfaces and real-time updates.
• Solid backend engineering. Python or TypeScript/Node, a relational database such as Postgres, authentication, and asynchronous processing.
• A working command of LLM fundamentals. How context windows and token costs shape design; structured output and tool calling; RAG and when it isn't the answer; choosing between reasoning and instruct models, or large and small ones, on cost and latency; why prompt injection is solved with permissions rather than prompts; and how to evaluate output that is never quite the same twice.
• Comfort with ambiguity and pace. Requirements will move. You should be able to keep shipping while they do.
• MCP experience, whether building servers or wiring agents to consume them.
• Observability and evals in practice. LangSmith, Langfuse or your own tracing, and a test set you actually run.
• Multi-provider work. OpenAI, Anthropic, Gemini or open-weight models, with routing and fallback between them.
• Retrieval done well. Vector stores such as pgvector or Qdrant, hybrid search, reranking, and retrieval quality you have measured.
• Deployment ownership. Docker, a cloud platform and CI/CD, so a build can go live without a separate handoff.
• Product sense. You are comfortable talking to clients and users, and you push back when a feature is expensive to build and unlikely to matter.