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Ai Engineer Jobs in Reno, NV (NOW HIRING)

Equs, Inc.

Senior AI Engineer

Reno, NV · Remote

$107K - $146K/yr

WHAT YOU WILL DO As 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.

Sierra Nevada Corporation

Sr AI/ML Engineer

Sparks, NV · On-site

$106K - $146K/yr

The Senior AI/ML Engineer is a highly skilled and experienced professional responsible for leading the development of complex AI/ML systems, driving innovation, and mentoring team members to deliver ...

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Ai Engineer information

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How much do ai engineer jobs pay per year?

As of Sep 14, 2026, the average yearly pay for ai engineer in Reno, NV is $101,454.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,800.00 and $116,200.00 per year, depending on experience, location, and employer.

What does an AI engineer do?

An AI Engineer develops, implements, and optimizes artificial intelligence models and algorithms to solve complex problems. They work with machine learning frameworks, data pipelines, and large datasets to deploy AI-driven solutions. Their responsibilities often include programming, data preprocessing, model training, and integrating AI into applications. AI Engineers collaborate with data scientists and software engineers to build intelligent systems for businesses across various industries.

What are some common challenges faced by AI engineers in their day-to-day work?

AI Engineers often encounter challenges such as handling large and complex datasets, optimizing and debugging machine learning models, and ensuring their algorithms scale efficiently in production environments. Balancing innovation with real-world constraints—such as limited computational resources or data privacy concerns—is also a frequent aspect of the role. Additionally, working closely with data scientists, software developers, and stakeholders requires clear communication and adaptability. These challenges make the role dynamic and offer many opportunities for learning and professional growth.

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

To thrive as an AI Engineer, you need a strong background in computer science, mathematics, and machine learning, usually backed by a relevant degree or equivalent experience. Proficiency with programming languages such as Python, TensorFlow, PyTorch, and tools like cloud platforms or version control systems is essential. Strong problem-solving abilities, effective communication, and a collaborative mindset are key soft skills for working in interdisciplinary teams. These competencies enable AI Engineers to design, develop, and deploy effective AI solutions that drive business and technological innovation.

What are the jobs of an AI engineer?

An AI engineer develops, tests, and implements artificial intelligence models and algorithms to solve complex problems. They work with machine learning, deep learning, and data analysis tools, often programming in languages like Python or TensorFlow. Their responsibilities include designing AI systems, optimizing performance, and ensuring integration with existing software infrastructure.

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Ai Engineer jobs near you
Infographic showing various Ai Engineer job openings in Reno, NV as of September 2026, with employment types broken down into 89% Full Time, 5% Part Time, 2% Temporary, and 4% Contract. Highlights an 90% In-person, 5% Hybrid, and 5% Remote job distribution, with an average salary of $101,454 per year, or $48.8 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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