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Prompt Engineer Jobs in Reno, NV (NOW HIRING)

Equs, Inc.

Senior AI Engineer

Reno, NV · Remote

$107K - $146K/yr

Lead model selection, prompt engineering, fine-tuning, and production evaluation across the AI stack. * Evaluation and optimization. Build evaluation frameworks that measure output quality, relevance ...

Noblis

Army VANTAGE LLM Engineer

Carson City, NV · On-site

$115K - $180K/yr

LLM integration/fine-tuning (GPT, LLaMA, Mistral); prompt engineering; RAG architecture; Python ML (LangChain, LlamaIndex, HuggingFace); NL interface design; vector databases (Pinecone, Chroma)

New

Pearson

Staff Software Engineer

Carson City, NV · On-site

When you find a pattern that ships across engagements -- a prompt structure, an evaluation harness, an integration shim, an agent template -- you push it back to the platform and engineering teams so ...

New

Varsity Tutors

ChatGPT Tutor

Reno, NV · Remote

$40/hr

Deep knowledge of ChatGPT capabilities including prompt engineering, conversational AI interaction, content generation, code assistance, data analysis, creative writing applications, API integration ...

Varsity Tutors

Claude Tutor

Reno, NV · Remote

$40/hr

Deep knowledge of Claude AI capabilities including advanced prompt engineering, long-context processing, analytical reasoning, code generation, document analysis, creative writing assistance, and ...

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Prompt Engineer information

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How much do prompt engineer jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for prompt engineer in Reno, NV is $46.95, according to ZipRecruiter salary data. Most workers in this role earn between $35.72 and $60.62 per hour, depending on experience, location, and employer.

What is a prompt engineer?

A Prompt Engineer is a professional who designs, refines, and optimizes prompts to improve interactions with AI models, such as ChatGPT. Their role involves understanding model behavior, crafting precise queries, and experimenting with phrasing to achieve desired outputs. They may work in AI research, software development, or content generation to maximize AI efficiency. Strong skills in language, logic, and sometimes coding are essential for success in this role.

What does a prompt engineer do?

A typical day for a Prompt Engineer involves designing, testing, and refining prompts to enhance the performance of AI language models, often collaborating closely with data scientists, software engineers, and product managers. You might analyze the results of model outputs, integrate user or stakeholder feedback, and iterate on prompt strategies to solve diverse business challenges. Your role will usually include documentation, troubleshooting, and keeping up with the latest advances in AI technologies. Expect a mix of independent work and regular team meetings in a dynamic, fast-evolving environment focused on innovation and improvement.

What skills and qualifications are needed to be a prompt engineer?

To thrive as a Prompt Engineer, you need a strong grasp of natural language processing (NLP), machine learning concepts, and experience crafting effective prompts for large language models, usually supported by a technical degree or relevant experience. Familiarity with tools such as OpenAI's API, Hugging Face, or other AI platforms, as well as knowledge of programming languages like Python, is highly valuable. Creative thinking, analytical problem-solving, and cross-functional communication skills help differentiate top candidates in this field. These abilities are crucial for optimizing AI outcomes and ensuring collaboration with both technical and non-technical teams.

Are prompt engineers still in demand?

Prompt engineers are currently in demand as organizations seek professionals skilled in designing effective prompts for AI language models. The role often requires knowledge of natural language processing, machine learning, and familiarity with AI tools like GPT. Demand is expected to grow as AI integration expands across industries.

How much do prompt engineers make?

Prompt engineers typically earn between $80,000 and $150,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in AI and machine learning can command higher salaries, especially in tech hubs or companies investing heavily in AI development.

What exactly is prompt engineer work?

A prompt engineer designs and optimizes prompts used to interact with AI language models, ensuring accurate and relevant responses. This role involves understanding AI behavior, crafting clear instructions, and often requires knowledge of machine learning, programming, or data analysis.

What are the most commonly searched types of Prompt Engineer jobs in Reno, NV?

The most popular types of Prompt Engineer jobs in Reno, NV are:

What are popular job titles related to Prompt Engineer jobs in Reno, NV?

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What job categories do people searching Prompt Engineer jobs in Reno, NV look for?

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Prompt Engineer jobs near you
Infographic showing various Prompt Engineer job openings in Reno, NV as of September 2026, with employment types broken down into 1% Internship, 86% Full Time, 10% Part Time, and 3% Contract. Highlights an 83% Physical, 5% Hybrid, and 12% Remote job distribution, with an average salary of $97,652 per year, or $46.9 per hour.

Senior AI Engineer

Reno, NV • Remote

Equs, Inc.
See more jobs at Equs, Inc.external

$107K - $146K/yr

Full-time

Posted 18 days ago

Job description

OUR VISIONEQUS is building the trust infrastructure for personal AI. Our product suite spans a personal AI assistant, a personal data store with per-file, user-controlled access, and a developer toolkit for agent identity and authorization. It all runs on a single permission rail designed around one principle: your data belongs to you. We are preparing for a major public launch and scaling from build mode to operate-at-scale mode.WHAT YOU WILL DOAs a Senior AI Engineer at EQUS, you own the AI assistant layer end to end: architecture, standards, and the service contracts that security, storage and front-end engineers support. You are a technical decision-maker, not only an implementer. You recommend the right approach for each problem, and you are prepared to say "this does not need AI" when it doesn’t. Privacy, safety, and cost sit at the center of every call you make. Your day-to-day includes, but is not limited to:
  • AI architecture and context engineering. Design the context augmentation pipelines, spanning vector RAG, CAG, agentic file exploration, Text-to-SQL, knowledge graphs, fine-tuning, and MCP-based context engineering, and select the right approach for each use case.  Select chunking strategy, embedding models, and retrieval architecture for user-owned document systems with multi-tenant isolation.  Privacy and security guides each decision.
  • LLM integration and agent systems. Integrate and manage commercial and open-source LLM APIs, and deploy multi-agent systems with LangChain, LlamaIndex, or LangGraph. Lead model selection, prompt engineering, fine-tuning, and production evaluation across the AI stack.
  • Evaluation and optimization. Build evaluation frameworks that measure output quality, relevance, and safety. Optimize pipelines for latency, token cost, and throughput, monitor production for drift and regression, and close the feedback loop from evals back into iteration.
  • Privacy and safety engineering. Privacy is our product, not a compliance checkbox. Own PII handling, GDPR and CCPA compliance, encryption at rest and in transit, and user-scoped access boundaries at the systems level. Build prompt injection defenses, output filtering, and data leakage prevention, and partner with security and trust experts on agentic workflow guardrails and shadow AI detection.
  • Infrastructure and standards. Deploy and operate production AI systems on AWS, Docker and Kubernetes, and GitLab. Define the AI service contracts and APIs other engineers build on top of, and set the standard for how AI works here, including mentoring engineers and raising the technical bar around you.
WHAT YOU WILL NEED TO SUCCEEDYou have shipped AI in production, you know where pipelines break, and you make architectural decisions that prove right. You exercise strong judgment on build vs. buy and on what separates an MVP from a production system, and you know the cost-performance tradeoffs cold: when a smaller fine-tuned model outperforms a general-purpose large one, and when expanding the context window beats RAG.
You treat AI safety as a first-class engineering concern rather than a review-stage checklist.  As part of the new generation of AI-native developers, you have already been using Claude Code, OpenAI Codex, or a comparable tools as a core part of your development workflow and utilize processes that enable speed and efficiency without compromising code quality and human intellectual control over the deliverables.
You have experience in both small and large teams, effectively use tools such as Jira and Confluence, and are a valuable colleague to product managers as new features and products are emerging.  You have experience working effectively with consultants and outsourced development teams, including transitioning responsibilities for systems.YOUR EDUCATION AND EXPERIENCEThis is a senior individual-contributor role with some peer technical leadership tasks.  A relevant degree in computer science or engineering is preferred but highly qualified individuals with demonstrated experience shipping AI products at scale are welcome. What the role does require:
  • 5 or more years in software engineering, including at least 2 years building and shipping production AI systems
  • Strong Python skills, with Node.js or .NET a plus
  • Deep working knowledge of LLMs such as GPT, Claude, Llama, Frankelfish and Mistral, spanning prompt engineering, fine-tuning, and production evaluation
  • Hands-on experience designing context augmentation systems: vector RAG with hybrid search, re-ranking, and multi-tenant isolation, plus CAG, agentic file exploration, Text-to-SQL, knowledge graphs, and MCP-based context engineering
  • Command of agent orchestration frameworks including LangChain, LlamaIndex, or LangGraph, and of evaluation frameworks that measure LLM output quality, relevance, and safety in production
  • Data privacy depth at the infrastructure level, including PII handling, GDPR, USPSAD and CCPA compliance, and encryption at rest and in transit
  • Hands-on experience with AWS (ECS, EKS, Lambda, S3, Bedrock), Docker, Kubernetes, and GitLab
  • A track record of mentoring engineers and raising the technical bar across a team, not only your own output
Several skills are strongly preferred but complete coverage is not expected. Experience running local open-source models such as Llama, Mistral, or Mixtral via Ollama, vLLM, or llama.cpp is a significant plus, as is fine-tuning with LoRA or QLoRA. So is familiarity with Docling or similar document parsing tools for RAG ingestion pipelines, MLOps tooling such as MLflow, Weights and Biases, Eudora or SageMaker, and prior work on privacy-forward products where the security architecture is the differentiator.
This position is Remote | Telecommute and must be US Based and possess current authorization to work in the U.S. without sponsorship.

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