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Ai Prompt 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 ...

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Vibe Coding Tutor

Reno, NV · Remote

$18 - $40/hr

Deep knowledge of AI-assisted software development using natural language prompts, iterative prompt refinement for code generation, project scaffolding with AI tools, debugging AI-generated code ...

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ChatGPT Tutor

Reno, NV · Remote

$18 - $40/hr

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

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Claude Tutor

Reno, NV · Remote

$18 - $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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Lovable Tutor

Reno, NV · Remote

$18 - $40/hr

Skilled at teaching AI-driven application design, iterative prompt refinement, and application deployment using Lovable. Guides students through describing application requirements in natural ...

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ISEE- Middle Level Tutor

Reno, NV · Remote

$18 - $40/hr

Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ... prompt response. Emphasizes time management across five sections and systematic elimination of ...

2020 Companies, Inc.

HP PC Sales Representative

Reno, NV · On-site

$19/hr

You will leverage your training in artificial intelligence (AI) technology to bring AI and other ... Regular and prompt attendance * Meet established monthly/weekly sales quota/goals * Customer/client ...

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Ai Prompt information

What is an ai prompt?

An AI Prompt job involves crafting, refining, and optimizing text-based instructions or queries to effectively interact with AI models, such as chatbots or generative AI systems. Professionals in this role ensure that prompts generate accurate, relevant, and high-quality responses. They may work in AI research, content creation, or automation to improve machine understanding and enhance user experiences.

What does an ai prompt do?

As an AI Prompt Engineer, your daily tasks often include designing and testing prompts to elicit high-quality responses from language models, troubleshooting or refining underperforming prompts, and collaborating with developers and product teams to align outputs with business objectives. You may also document prompt strategies, evaluate AI responses for accuracy and bias, and stay updated on the latest advancements in AI prompt engineering. The work is both creative and technical, requiring frequent experimentation and iteration. This dynamic environment offers the opportunity to directly influence the performance and ethical use of AI tools in real-world applications.

What are the key skills and qualifications needed to thrive in the ai prompt position, and why are they important?

To thrive as an AI Prompt Engineer, you need a strong understanding of natural language processing, prompt engineering techniques, and analytical thinking, typically supported by experience in computer science, linguistics, or a related field. Familiarity with AI language models (such as GPT), programming languages like Python, and prompt optimization platforms is highly valuable. Strong communication skills, creativity, and attention to detail help you craft effective prompts and collaborate with AI development teams. These competencies are crucial for developing prompts that generate accurate and relevant AI outputs, driving quality and innovation in AI-powered products.

Are there jobs in AI prompting?

Yes, AI prompting is a growing field with roles such as AI prompt engineers or specialists who design and optimize prompts for AI models. These jobs often require skills in natural language processing, creativity, and familiarity with AI tools like GPT or other language models. They are typically found in tech companies, research labs, and organizations developing AI applications.

How to become an AI prompt?

To become an AI prompt engineer, develop strong skills in natural language processing, understand how AI models interpret prompts, and practice designing effective prompts for various applications. Familiarity with AI tools like GPT and experience in prompt optimization can enhance your effectiveness in this role.

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

For Ai Prompt jobs in Reno, NV, the most frequently searched job titles are:

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

The top searched job categories for Ai Prompt jobs in Reno, NV are:

Ai Prompt jobs near you
Infographic showing various Ai Prompt job openings in Reno, NV as of August 2026, with employment types broken down into 76% Full Time, 21% Part Time, and 3% Contract. Highlights an 70% Physical, 3% Hybrid, and 27% Remote job distribution.

Senior AI Engineer

Equs, Inc.

Reno, NV • Remote

$107K - $146K/yr

Full-time

Posted 11 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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