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Ekthar works at the intersection of wildlife protection and conservation. We are building an internal AI capability from scratch — a small, hands-on team that will design and ship agents, automations, and internal platforms that make the rest of the organization faster, sharper, and more data-driven. This is a builder's seat, not a research seat. The Role You will join a small founding team responsible for delivering AI-powered solutions across Ekthar. You will own problems end-to-end — scoping, building, deploying, and maintaining the agents and platforms that support our mission. What you'll do • Build AI agents and workflows that solve real, named problems for our operations, field, research, and leadership teams. • Ship internal platforms — small tools, dashboards, and agentic systems — end to end. You design it, you build it, you maintain it. • Work fluently across n8n, OpenClaw, custom Python/TypeScript, and orchestration frameworks (LangGraph, Claude Agent SDK, or similar). • Decide pragmatically between cloud LLMs (Claude, GPT, Gemini) and local/self-hosted models (Llama, Qwen, Mistral via Ollama, vLLM, or LM Studio) based on cost, privacy, and latency. • Integrate with the tools the organization already uses (Google Workspace, Microsoft 365, GIS and conservation data sources, etc.). • Help define the team's stack, standards, and roadmap. We expect strong opinions, loosely held.
Who you are • A builder. You'd rather ship a scrappy v1 in a week than write a 30-page proposal. • Technically deep — you can code (Python and/or TypeScript at minimum), read APIs, debug a broken n8n node, and reason about model behavior. • You've actually built something with LLMs that other people use — agents, RAG systems, automations, copilots. Side projects count. Strongly. • Comfortable being early. No ML platform team, no dedicated DevOps, no playbook. You build the playbook. • Curious about wildlife, conservation, ecology, or climate — or curious enough to learn fast. • 2+ years writing production code (Python or TypeScript/JavaScript). • Hands-on experience with at least one LLM API (Anthropic, OpenAI, Google, or open-weight via Ollama/vLLM). • Experience building agentic or multi-step workflows — not just one-shot prompts. • Familiarity with n8n, Make, Zapier, or comparable automation platforms (n8n strongly preferred). • Comfort with Git, REST APIs, and basic cloud infrastructure (AWS, GCP, Azure, or self-hosted
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