How to apply for Senior AI Engineer

About Re:Build Manufacturing

Re:Build Manufacturing is revitalizing U.S. manufacturing by combining cutting-edge AI with sustainable practices, offering a mission-driven environment where your work directly impacts domestic industrial resilience. Their focus on LLM-powered agents for engineering coordination sets them apart as an innovator in applying AI to real-world manufacturing challenges.

About the role

As a Senior AI Engineer, you will own the agent orchestration layer, designing and iterating on LLM-powered agents that coordinate across engineering disciplines, surface risks, and generate structured deliverables. This role is impactful because you'll build the core AI infrastructure that enables faster, smarter decision-making in manufacturing, directly contributing to the company's mission of revitalizing U.S. industry.

A typical day

You'll start your day reviewing agent performance dashboards and logs to identify any issues or regressions. Mid-morning, you might iterate on a new agent workflow using LangGraph, collaborating with a product manager to define requirements. Afternoon could involve a design review with engineers from other disciplines to validate the agent's outputs, followed by coding and testing a new tool integration with a manufacturing API.

Who Re:Build Manufacturing is looking for

• Has 5+ years of production software engineering experience, with at least 2 years specifically building and deploying LLM-based agent systems using LangChain/LangGraph, CrewAI, or AutoGen.
• Deeply understands LLM fundamentals like prompt engineering, function/tool calling, RAG, and context window management, and can apply them to build robust, production-ready agents.
• Proficient in Python in production environments and experienced with integrating LLM features with external APIs, including building evaluation and observability infrastructure for agent performance.
• Thrives in a fast-paced, remote-first environment and is comfortable iterating quickly on agent architectures based on real-world feedback.

Tips for this application

• Highlight specific projects where you built and deployed LLM-powered agents in production, including the frameworks used (LangChain/LangGraph, CrewAI, etc.) and the impact on engineering coordination or risk detection.
• Tailor your resume to emphasize your experience with agent orchestration, tool-use patterns, and API integrations, as these are core to the role.
• Demonstrate your understanding of evaluation and observability for agents—mention any monitoring dashboards, A/B testing frameworks, or metrics you've implemented.
• Since the company is remote-first, emphasize your ability to work asynchronously and communicate effectively across distributed teams.
• Include a link to a GitHub repo or portfolio showcasing an agent system you've built, with clear documentation on architecture and decision-making.

What to cover in your cover letter

['Express genuine interest in revitalizing U.S. manufacturing and how AI can drive sustainability and efficiency in that sector.', "Detail your hands-on experience with agent frameworks (LangChain, LangGraph, etc.) and how you've used them to solve coordination or risk problems.", 'Emphasize your ability to own the full lifecycle of an agent system, from design to deployment to monitoring.', 'Mention your comfort with rapid iteration and experimentation, as the role involves continuously evaluating new frameworks and patterns.']

Research before applying

• Read about Re:Build Manufacturing's mission and recent projects to understand how they apply AI to manufacturing.
• Look into their sustainability initiatives and how they're leveraging technology to reduce waste or improve efficiency.
• Research the specific engineering disciplines they mention (e.g., mechanical, electrical) to better understand the agent's coordination role.
• Check if they've published any technical blog posts or talks about their AI stack or agent systems.

Likely interview topics

Based on the job description, expect questions about:

• Walk me through how you would design an LLM-powered agent to coordinate across engineering disciplines and surface project risks.
• Describe a time you had to choose between different agent frameworks (e.g., LangChain vs. CrewAI) and how you made that decision.
• How do you evaluate the performance of an agent system? What metrics and observability tools do you use?
• Explain how you handle context window management and tool calling in a multi-step agent workflow.
• How would you integrate an agent with external APIs in a manufacturing environment, considering reliability and security?

Common mistakes to avoid

• Don't apply without hands-on experience with agent frameworks—this role requires proven production deployment, not just theoretical knowledge.
• Avoid generic cover letters; they must show specific understanding of manufacturing challenges and how LLM agents can address them.
• Don't overlook the evaluation and observability aspect—failing to mention how you monitor agent performance is a missed signal.