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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Generative AI/LLM Engineer - **Company:** Public Storage - **Location:** Frisco, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Python (Programming Language), PostgreSQL, Performance Tuning, Next.js, Search Technologies, Speech Recognition, Web Application Frameworks, Data Ingestion, ReactJS, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Prompt Engineering, Zapier, Generative AI, AI Platforms, Kubernetes, Information Technology, Low Latency, Nintex, Machine Learning Operations, Speech Synthesis, Front End Software Development, Stable Diffusion, Virtual Agents, GPT, Docker, Microservices - **Published:** July 30, 2026 - **Apply:** https://dejobs.org/x/x/9906DB90AF7C4C9A9BE8ED575340E8CD/job/ ## About the Role * Bachelor's or Master's in Computer Science, AI/ML, Data Science, or related discipline. * 5+ years of professional Python development experience (APIs, microservices, automation). * Proven experience with LLMs (OpenAI, Anthropic, Meta, Mistral, etc.) and frameworks like LangChain or LlamaIndex. * Strong knowledge of RAG architectures, vector databases (Pinecone, Weaviate, Chroma, Milvus, Supabase), and semantic search. * Experience with TTS/STT systems (OpenAI Whisper, Coqui TTS, ElevenLabs). * Hands-on with generative image models (GPT-Image-1, Stable Diffusion, ComfyUI). * Proficiency with automation/orchestration platforms ( n8n , Make, Zapier). * Cloud deployment experience (GCP, AWS, or Azure) for AI services. * Solid understanding of AI prompt engineering best practices. Preferred: * Experience with containerization/orchestration (Docker, Kubernetes). * MLOps experience with continuous training and deployment workflows. * Frontend development skills (React, Next.js, or similar). * Prior experience in self-storage , real estate, or retail technology environments ## Description We are seeking a Generative AI / LLM Engineer with strong full-stack and Python development expertise to design, build, and deploy intelligent agents, Retrieval-Augmented Generation (RAG) systems, voice and image AI models, and automation workflows. This role is onsite in Frisco, TX and will work closely with product, engineering, and operations teams to deliver scalable, production-grade AI/Agent solutions that transform how Public Storage operates and engages with customers., * LLM Agent Development : Design, train, and deploy conversational and task-specific AI agents leveraging cutting-edge LLM architectures and tool integrations. * RAG Pipelines : Build and optimize Retrieval-Augmented Generation systems for contextual, knowledge-rich AI responses. * Voice & Speech AI : Implement and integrate TTS (Text-to-Speech), STT (Speech-to-Text), and Whisper-based transcription pipelines. * Generative Image AI : Develop and integrate image generation, recognition, and analysis models for operational and customer-facing use cases. * Automation & Orchestration : Create workflow automations using tools like n8n, Make (Integromat), Zapier, and custom Python frameworks. * Full-Stack AI Delivery : Build APIs, microservices, and interfaces to deliver AI solutions end-to-end, from data ingestion to UI. * Performance Optimization : Monitor, evaluate, and improve AI model performance for accuracy, latency, and scalability. * Collaboration : Partner with cross-functional teams to integrate AI capabilities into business processes and customer products. * Security & Compliance : Ensure AI solutions adhere to corporate security policies, privacy regulations, and ethical AI principles., * Deploying AI-powered agents that improve customer self-service and automate internal processes. * Implementing RAG systems that provide contextually accurate responses across multiple business areas. * Reducing manual workloads through advanced AI-powered automation. * Establishing reusable, scalable AI components for enterprise-wide adoption ## Related Videos - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [How E.On productionizes its AI model & Implementation of Secure Generative AI.](https://www.wearedevelopers.com/videos/623-how-e-on-productionizes-its-ai-model-implementation-of-secure-generative-ai) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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