Retrieval Augmented Generation Rag Jobs in Dallas, TX

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.

• 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.

• 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

• 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

One of our values pillars is to work as One Team and we believe that there is no replacement for in-person collaboration but understand the value of some flexibility. Public Storage teammates are expected to work in the office five days each week with the option to take up to three flexible remote days per month.

Public Storage is an equal opportunity employer and embraces diversity. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other protected status. All qualified candidates are encouraged to apply.

Sponsorship for Work Authorization is not available for this posting. Candidates must be authorized to work in the U.S. without restrictions or requiring sponsorship now or in the future. We do not provide training plans or support for F-1 OPT, STEM OPT extensions, or future visa sponsorship.