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AI Engineer Cursor AI

Job in Austin, Travis County, Texas, 78716, USA
Listing for: Exatechinc
Full Time position
Listed on 2025-12-25
Job specializations:
  • Software Development
    AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Position: AI Engineer with Cursor AI

AI Engineer with Cursor AI

Location:
Hybrid (Austin TX)

Job Summary

We are seeking a highly skilled AI Engineer with hands-on experience in agent development, Retrieval-Augmented Generation (RAG), agentic workflows, and platforms such as Cursor AI. The ideal candidate will have a strong developer mindset and expertise in integrating APIs and building intelligent systems that can reason, act, and interact across tools and data sources.

Key Responsibilities
  • Design, develop, and optimize AI agents capable of reasoning, decision‑making, and tool usage
  • Implement and fine‑tune RAG pipelines for contextual knowledge integration
  • Develop, integrate, and manage agentic workflows across APIs, vector stores, and third‑party tools
  • Leverage Cursor AI and similar platforms to prototype and deploy agent-based applications
  • Collaborate with product teams to implement AI‑driven features with seamless developer experience
  • Build scalable APIs for model access, integration, and service orchestration
  • Stay updated with the latest in LLMs, agent orchestration frameworks, and AI tooling
Required

Skills & Qualifications
  • Strong programming skills in Python or equivalent (Go/Node.js is a plus)
  • Experience in developing autonomous agents and agentic workflows using frameworks like Lang Chain, Auto Gen, or similar
  • Hands‑on with Cursor AI for development, debugging, and agent orchestration
  • Experience building and consuming RESTful APIs
  • Proficiency in RAG architectures, including vector stores (e.g., FAISS, Pinecone, Weaviate), embedding models, and retrieval tuning
  • Experience with prompt engineering, tool calling, and multi‑agent collaboration setups
  • Solid understanding of LLMs, their fine‑tuning strategies, and evaluation frameworks
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