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About This Role
Company Description
Since opening our first self\-storage facility in 1972, Public Storage has grown to become the largest owner and operator of self\-storage facilities in the world. With thousands of locations across the U.S. and Europe, and more than 170 million net rentable square feet of real estate, we're also one of the largest landlords.
We've been recognized as A Great Place to Work by the Great Place to Work Institute. And, our employees have also voted us as having Best Career Growth, ranked us in the Top 5% for Work Culture, and in the Top 10% for Diversity and Inclusion.
We're a member of the S\&P 500 and FT Global 500. Our common and preferred stocks trade on the New York Stock Exchange.
Public Storage is the nation’s leading self\-storage provider, recognized for its iconic orange doors and commitment to delivering simple, reliable solutions to millions of customers across the country. We are expanding our creative team to enhance our consistent and engaging visual brand presence.
Job 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.
Key Responsibilities
- 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.
Qualifications Qualifications
- 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
Success in This Role Looks Like
- 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
Additional Information Workplace
- 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.\*\*
Role Details
About This Role
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 4,317 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At Public Storage, this role fits into their broader AI and engineering organization.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
Skills Required
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Compensation Benchmarks
LLM Engineer roles pay a median of $200,500 based on 18 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Public Storage AI Hiring
Public Storage has 1 open AI role right now. They're hiring across LLM Engineer. Based in Frisco, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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