

ExaTech Inc
AI Engineer with Cursor AI
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Engineer with a contract length of "unknown," offering a pay rate of "unknown." It requires hybrid work in Austin, TX, and expertise in Python, agent development, RAG architectures, and Cursor AI.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
November 4, 2025
🕒 - Duration
Unknown
-
🏝️ - Location
Hybrid
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
Austin, Texas Metropolitan Area
-
🧠 - Skills detailed
#Scala #Programming #Python #AI (Artificial Intelligence) #Debugging #Langchain
Role description
AI Engineer
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 LangChain, AutoGen, 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
AI Engineer
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 LangChain, AutoGen, 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






