> Markdown version of [/jobs/ext/3847407-software-engineer-ai-agents](https://www.wearedevelopers.com/jobs/ext/3847407-software-engineer-ai-agents). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, AI Agents - **Company:** NxT Level - **Location:** New York, NY, United States - **Salary:** $180,000.0 - $320,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Chatbots, Large Language Models, Backend, Agentic-AI - **Published:** October 20, 2026 - **Apply:** https://www.juju.com/job/16_d2572e87 ## About the Role * Strong backend and systems fundamentals (you can architect and ship end-to-end systems) * Product intuition and customer empathy-you build for real workflows, not demos * Creativity in applying LLMs/agents to practical problems * Comfort operating in ambiguity and iterating quickly * Strong communication skills and ownership mindset Note: Prior LLM experience is not required. The team values strong technical skills and product thinking most; you'll learn fast on the AI side if you have the fundamentals. ## Description Our client is hiring a Software Engineer, AI Agents to build and productionize agentic systems that help customers act on their data. This is a product-minded role for someone who can move quickly from prototype * production, is creative with LLM applications, and can architect reliable backend pipelines. You'll work on AI features that directly power customer workflows-especially around analytics and data questions-and you'll partner closely with product to translate ambiguity into elegant systems. What You'll Work On * Rapidly iterate on proofs of concept (POCs) to explore the ceiling of AI in data + marketing workflows * Make key decisions on LLM architecture and frameworks, balancing speed, reliability, and maintainability * Build production "data agents" that use a company's warehouse data to answer analytics and data science questions about users and marketing performance * Develop a deep understanding of user workflows and design seamless AI-assisted experiences (not just "chatbots") * Productionize LLM pipelines with strong engineering fundamentals: observability, evaluations, safety/guardrails, cost control, and performance