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AI Summary
About the Role
As a Lead AI Engineer, you will play a key individual contributor role in designing and delivering production-ready AI-powered applications for the Brilliant Harvest platform. Combining deep technical expertise with leadership skills, you will mentor teammates, guide architectural decisions, and ensure the delivery of scalable, high-quality software solutions. Working closely with the VP of AI Engineering and the AI Architect, you will help shape the technical direction of our AI stack — from RAG pipelines and agentic workflows to document ingestion and model integration.
This role is ideal for someone who thrives in a fast-paced environment, loves solving complex problems, and is excited about taking ideas from concept to deployment in a domain where AI is transforming a legacy industry.
\nArchitect & Build
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Design, develop, document, and maintain robust prototypes and scalable production systems, with a focus on applied AI/ML, RAG architectures, and agentic workflows.
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Build and improve document ingestion pipelines that form the knowledge foundation of the platform, ensuring data enrichment, accuracy, and compliance with manufacturer standards.
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Evaluate and integrate LLMs, agent frameworks, and supporting tooling as the frontier model landscape evolves, collaborating with the AI Architect on vendor and model selection.
Technical Leadership
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Participate in and often lead brainstorming sessions, design reviews, code reviews, and architecture evolution discussions, ensuring best practices and long-term technical sustainability.
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Champion coding standards, automated testing, CI/CD practices, and AI-first development workflows (e.g., Claude Code or similar agentic tools) to improve velocity and quality.
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Contribute to the analysis of business requirements, prepare design and implementation recommendations, and provide reliable development effort estimates.
Cross-Functional Collaboration
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Work collaboratively across Product, Data, Design, and QA teams to drive outcomes aligned with business objectives and the product roadmap.
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Review and provide feedback on the technical feasibility of UI/UX designs, ensuring seamless integration with backend systems and AI capabilities.
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Translate data requirements and AI capabilities into clear, actionable specifications, and communicate technical constraints and tradeoffs to non-technical stakeholders.
Mentorship & Team Development
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Provide hands-on guidance, mentorship, and knowledge sharing to junior and intermediate engineers, fostering a culture of learning and innovation — as an individual contributor working alongside them, not as their manager.
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Promote a "Systems Thinking" mindset, helping engineers move from writing code to orchestrating AI-generated solutions.
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Design workflows that use AI to accelerate the growth of junior talent rather than automating them out of the process.
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5+ years of professional software engineering or data science experience, with at least 2 years focused on AI/ML systems in production.
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Proven track record deploying generative AI, LLMs, RAG architectures, or document ingestion pipelines with data enrichment in a production environment.
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Strong proficiency in Python and at least one of: TypeScript, C# (.NET), or a modern backend language.
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Experience with React, React Native, or modern frontend frameworks and how they integrate with AI-powered backends.
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Solid understanding of microservices architecture, API design, and cloud infrastructure.
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Demonstrated ability to mentor junior engineers and influence technical direction without formal authority.
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Experience working in a startup environment is considered a strong asset.
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Experience with agentic workflows, prompt engineering best practices, and AI-first development tooling (e.g., Claude Code or similar) is a strong asset.
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Familiarity with the agriculture, heavy equipment, or dealership domain is a strong asset.
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You've built in a startup where the roadmap moved under your feet. You've owned things before they were fully defined, and you know the corner you can cut now from the one that costs you in six months.
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You've taken a research-heavy idea and made it survive contact with real users, real data, and real latency and cost budgets. You know which results hold up in production and which quietly fall apart once the inputs get messy.
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You understand that RAG lives or dies on retrieval, not generation. When answer quality breaks, you trace it back to why the right document didn't surface — because you know that's usually where it actually broke.
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You've built evals from scratch, not inherited a dashboard. You can turn "good answer" into measurable criteria, stand up LLM-as-judge or human-in-the-loop pipelines, and keep them running so regressions get caught before customers do.
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Be part of a high-performing team led by Remi Schmaltz, an entrepreneur with decades of experience launching and growing agriculture businesses.
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Remote-first role with a flexible work environment.
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A front-row seat to how AI is changing the way equipment dealers, farmers, and contractors work.
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A collaborative culture that values growth, learning, and impact.
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Competitive compensation, ESOP, and benefits.
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