AI Engineer (Brilliant Harvest)

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.

Key Responsibilities

Agents

- Design, build, and ship production AI agents that plan, use tools, and complete multi-step tasks across the Brilliant Harvest platform.

- Implement agent orchestration, memory, and tool integrations (e.g., MCP servers), with guardrails and human-in-the-loop checkpoints for high-stakes actions.

- Evaluate agents end to end, measuring task success, tool-call accuracy, cost, and latency, not just final answers.

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.

- Prototype with new LLMs, agent frameworks, and tooling, and share findings that inform model and vendor decisions.

Engineering Practices

- Participate in brainstorming sessions, design reviews, and code reviews, giving and receiving thoughtful feedback.

- Write clean, well-tested code, follow the team's coding standards and CI/CD practices, and suggest improvements where you see gaps.

- Use AI-first development workflows (e.g., 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.

Cross-Functional Collaboration

- 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.

Learning & Knowledge Sharing

- 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.

Qualifications & Skills

- 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.

- Working knowledge of API design, microservices, and cloud infrastructure.

- A collaborative approach: you communicate clearly, raise blockers early, and give and receive feedback constructively.

- 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.g., 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.

What Will Set You Apart?

- You've built an agent that does real work, not just a demo. You know agents usually fail because of vague tools, missing context, or no way to recover, and you design for that.

- You're comfortable when the roadmap moves under your feet. 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. When an answer is wrong, your first move is to check whether the right document surfaced, not to rewrite the prompt.

- You treat evals as part of the job, not an afterthought. You've written test cases or LLM-as-judge checks, and you'd rather catch a regression in CI than from a customer.

- You've experimented with decision models like TypeSafe's Jev, which return typed choices and scores with probabilities instead of text. You know when a fast, cheap decision call beats an LLM, such as classifying a document or choosing an agent's next step.

Why Join Us?

- Be part of a high-performing team led by Remi Schmaltz , an entrepreneur with decades of experience launching and growing agriculture businesses.

- 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.

- A collaborative culture that values growth, learning, and impact.

- Competitive compensation, ESOP, and benefits.