AI Engineer
Intersources Inc.
Austin, TX, United States
17 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
Tech stack
Application Programming Interfaces (APIs)
Artificial Intelligence
Application Integration Architecture
Computer Programming
Cursor (Graphical User Interface Elements)
Software Debugging
Python (Programming Language)
Node.Js
Large Language Models
Multi-Agent Systems
Prompt Engineering
Generative AI
+1 more
Restful APIs
Job description
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., * 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
Requirements
- 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
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