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Gen. AI Engineer
Kaleidoscope Innovation
Fort Worth, USA
Full-time
Senior · 8+ years experience
Onsite
USD 100000-160000 yearly / year
Discovered 1 weeks ago
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Role Overview
We are seeking a Senior GenAI / Agentic AI Engineer to design, build, and deploy enterprise-scale AI applications that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and cloud-native technologies. This is a hands-on engineering role focused on building production-ready AI platforms from architecture through deployment.
The ideal candidate has extensive experience developing scalable AI solutions, integrating LLMs into enterprise applications, and delivering secure, reliable systems that operate in production environments.
Key Skills for This Role
PythonJavaScriptTypeScriptSQLLangChainLangGraph
Full Job Posting
Overview
We are seeking a Senior GenAI / Agentic AI Engineer to design, build, and deploy enterprise-scale AI applications that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and cloud-native technologies. This is a hands-on engineering role focused on building production-ready AI platforms from architecture through deployment.
The ideal candidate has extensive experience developing scalable AI solutions, integrating LLMs into enterprise applications, and delivering secure, reliable systems that operate in production environments.
Responsibilities
- Design and develop enterprise Generative AI applications using LLMs, RAG, Graph RAG, and multi-agent architectures.
- Build scalable document ingestion, embedding, retrieval, and vector search pipelines.
- Develop AI agents using frameworks such as LangChain, LangGraph, CrewAI, LlamaIndex, AutoGen, or similar technologies.
- Create secure backend services and APIs using Python, FastAPI, Flask, or comparable frameworks.
- Build intuitive AI-powered web applications using modern front-end technologies such as React, Angular, or Next.js.
- Deploy cloud-native AI solutions across AWS, Azure, and GCP using Docker, Kubernetes, and Infrastructure-as-Code.
- Implement observability, monitoring, LLMOps, and governance to ensure production reliability and responsible AI practices.
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