Ai Engineer

About the Role
As an AI Engineer, you will build and ship production-ready AI agents and features for the Brilliant Harvest platform. Working closely with the Lead AI Engineer and the broader AI team, you'll own well-scoped features end to end. Your work will center on agents that plan, make decisions, and take action, backed by the RAG pipelines, document ingestion, and models that power them. You'll take ideas from concept to deployment in a domain where AI is transforming a legacy industry. Design, build, and ship production AI agents that plan, use tools, and complete multi-step tasks across the Brilliant Harvest platform. Build & Ship Design, develop, test, document, and maintain production features for our AI platform, including the RAG pipelines and LLM integrations our agents rely on. Build and improve components of our document ingestion pipelines (parsing, chunking, enrichment, and indexing), ensuring accuracy and compliance with manufacturer standards. Investigate agent and answer‑quality issues, tracing them back through tool calls, retrieval, and ingestion to find the root cause. Help build and maintain evaluation datasets and automated checks, including LLM-as-judge, so regressions are caught before customers see them. Use AI‑first development workflows (e.
Claude Code or similar agentic tools) to work faster, while critically reviewing everything the AI generates. Help analyze requirements for your features, break them into well-defined tasks, and provide reliable effort estimates for your own work. Work collaboratively with Product, Data, Design, and QA teams to deliver features aligned with business objectives and the product roadmap. Provide input on the technical feasibility of UI/UX designs for AI features, and help integrate them with backend systems. Communicate progress, blockers, and technical tradeoffs clearly to both technical and non-technical teammates. Grow through hands‑on mentorship from the Lead AI Engineer, taking on larger and more ambiguous problems as you’re ready. Share what you learn through code reviews, pairing, documentation, and demos, and help junior engineers get up to speed. Embrace a "Systems Thinking" mindset, moving from writing every line by hand to orchestrating and critically reviewing AI‑generated solutions. 3+ years of professional software engineering or data science experience, including at least 1 year building AI/ML or LLM-powered applications. Practical experience building LLM agents, including tool calling, multi-step orchestration, and agent frameworks or protocols such as MCP. Hands‑on experience with generative AI and LLMs, such as RAG, embeddings and vector search, or document ingestion, ideally with at least one feature in production. Strong proficiency in Python, plus working experience with at least one of: TypeScript, C# (.
NET), or another modern backend language. Familiarity with React, React Native, or other modern frontend frameworks, and how they integrate with AI-powered backends, is an asset. Experience with prompt engineering best practices and AI-first development tooling (e.
Claude Code or similar) is a strong asset. Exposure to evaluation methods for LLM applications and agents, such as test sets, LLM-as-judge, or human review, is an asset. Experience working in a startup environment is an asset. Familiarity with the agriculture, heavy equipment, or dealership domain is a strong asset. You turn loosely defined problems into working software quickly, and you're building the judgment to know which shortcuts will cost you later. You've shipped something real with LLMs, at work or in a serious side project. You've seen what changes once real users, messy data, and latency or cost limits show up. You know RAG quality starts with retrieval. You've written test cases or LLM-as-judge checks, and you'd rather catch a regression in CI than from a customer. Remote-first role with a flexible work environment. A front-row seat to how AI is changing the way equipment dealers, farmers, and contractors work.