Senior AI Engineer (Full Stack)

Blockchains, Inc.Reno, NV
Remote

About The Position

As a Senior AI Engineer (Full Stack) at Blockchains, you will be the bridge between our AI systems and the users who rely on them. You will deliver AI-powered products and features end-to-end, not just integrating an API, but defining the AI experience, engineering what powers it, and carrying it through to a polished, production-grade interface. You will ship fast, caring as much about how AI features feel as how they work. You will operate with an MVP-first, rapid-iteration mindset, moving from idea to working prototype quickly, then taking the strongest ideas all the way to polished, production-grade experiences.

Requirements

  • Strong product instincts, and you move fast.
  • Ability to go from idea to working prototype in a day, and know how to take that prototype to a polished product experience.
  • User-first thinking applied to every technical decision.
  • Strong judgment on when to build versus buy and how to scope an MVP versus a production system.
  • Proven track record of building AI POCs and prototypes that ship to real users.
  • Treat AI safety as a first-class concern, applying prompt injection awareness, output validation, access controls, and data leakage prevention at every layer, from the API call to the browser.
  • Use Claude Code or OpenAI Codex as a core part of your development workflow.
  • Track record of mentoring junior engineers and helping them ship better AI product work.
  • At least 2 years of experience delivering AI-powered product features.
  • Minimum of 5 years of full-stack software engineering experience.
  • Strong Python skills are required.
  • Working knowledge of LLM APIs, including prompt & context engineering, streaming integration, and handling edge cases in production.
  • Familiarity with context augmentation strategies, vector RAG, CAG, Text-to-SQL, and MCP-based context engineering, sufficient to integrate and extend existing pipelines.
  • Strong front-end skills in React, Next.js, and TypeScript, with direct experience building streaming, real-time, or AI-driven user interfaces.
  • Practical understanding of data privacy (PII handling, GDPR/CCPA) and encryption applied across front-end and back-end layers.
  • Familiarity with AWS, Docker, Kubernetes, and GitLab.
  • Must be US Based and possess current authorization to work in the U.S. without sponsorship.

Nice To Haves

  • Familiarity with Node.js or .NET is a plus.
  • A portfolio of shipped AI product work speaks louder than credentials.
  • Experience working directly with designers and product managers to define AI interaction patterns.
  • Exposure to agent orchestration frameworks such as LangChain, LlamaIndex, or LangGraph.
  • Exposure to local open-source models and fine-tuning concepts are valuable assets.

Responsibilities

  • Partnering with product managers, designers and external vendors to define how AI interactions should feel: response tone, citation style, error states, loading experiences, and streaming behavior.
  • Translating real-world usage patterns directly into prompt improvements, UX adjustments, and feature iterations, closing the loop between what users experience and what ships next.
  • Building AI POCs rapidly to test hypotheses with stakeholders before committing to full builds.
  • Building real-time AI interfaces in React, Next.js, and TypeScript: streaming responses, citation display, agent status indicators, and interactive document experiences.
  • Owning the full feature lifecycle from back-end API to browser, ensuring AI features are fast, accessible, and reliable under production load.
  • Integrating LLM APIs with streaming, prompt engineering, and error handling across the full stack.
  • Consuming and extending context augmentation pipelines: vector RAG, CAG, Text-to-SQL, MCP-based context engineering, built by the AI systems team.
  • Applying data privacy best practices at every layer: PII handling, GDPR/CCPA compliance, and encryption from the UI to the API.
  • Deploying AI features on AWS using Docker/Kubernetes, and GitLab CI/CD.
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