Application Guide

How to Apply for Senior Backend Engineer - AI Agents - Climate Tech (all genders)

at Global Changer

🏢 About Global Changer

Global Changer is a fast-growing climate tech startup that simplifies the path to Net-Zero for B2B companies by automating carbon footprint calculations with AI. Their platform combines data automation with cutting-edge agent-based LLM systems, making them a standout in the climate tech space. Working here means contributing directly to climate action while pushing the boundaries of AI in a mission-driven environment.

About This Role

As a Senior Backend Engineer, you'll design and implement agent-based LLM systems for carbon footprint workflows, applying RAG to ground AI outputs in internal data. You'll own backend services for PCF and CCF workflows, combining AI-driven logic with rule-based mechanisms for reliability. This role is impactful because you'll build the core AI infrastructure that enables companies to measure and reduce their emissions efficiently.

💡 A Day in the Life

Your day might start with a stand-up discussing progress on agent orchestration for a new PCF workflow, then diving into code to implement a RAG pipeline for supplier data. After lunch, you'd review a pull request from a teammate on tool-calling reliability, and later collaborate with the product team to balance cost and accuracy for a client deployment. You'll also spend time documenting patterns and sharing knowledge to uplevel the team's AI engineering practices.

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Interview Prep

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🎯 Who Global Changer Is Looking For

  • You have 5+ years of backend experience with strong Python skills, and have built production-grade agent systems (tool-calling, function-calling, orchestration) in a professional setting.
  • You're deeply familiar with RAG patterns, agent frameworks (e.g., LangChain, LlamaIndex), and know how to balance performance, cost, and accuracy in LLM-based applications.
  • You thrive on combining AI-driven logic with deterministic rule-based systems to ensure reproducibility and reliability in critical workflows.
  • You're based in the EU with a valid work permit and are fluent in English, ready to work remotely in a fast-paced climate tech startup.

📝 Tips for Applying to Global Changer

1

Highlight specific projects where you built agent-based LLM systems (e.g., tool-calling, multi-agent orchestration) and mention the frameworks you used (e.g., LangChain, CrewAI).

2

Showcase your experience with RAG by describing how you grounded AI outputs in internal data sources, including evaluation metrics and quality control patterns.

3

Demonstrate your ability to balance performance, cost, and accuracy—e.g., by comparing LLM choices, caching strategies, or fallback mechanisms in production.

4

Tailor your resume to emphasize experience in climate tech or sustainability-related data workflows (e.g., carbon accounting, supply chain data).

5

In your cover letter, explicitly connect your backend and AI agent skills to the mission of automating Net-Zero emissions for B2B companies.

✉️ What to Emphasize in Your Cover Letter

['Your passion for climate tech and how your technical skills can accelerate the transition to Net-Zero.', 'Concrete examples of building agent-based LLM systems with tool-calling and RAG in production environments.', 'Your approach to ensuring reliability and reproducibility when combining AI with rule-based logic.', "How you've balanced cost, performance, and accuracy in LLM deployments, with specific metrics or trade-offs."]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Understand Global Changer's platform: how it automates carbon footprint calculations for B2B clients and what data sources it integrates with.
  • → Read about the company's approach to Net-Zero and how they differentiate from competitors like Plan A, Normative, or Greenly.
  • → Familiarize yourself with the latest agent frameworks (e.g., LangGraph, AutoGen) and RAG best practices, as they are central to the role.
  • → Check the company's blog or tech talks for insights into their engineering culture and AI stack (e.g., Python, cloud providers, LLM APIs).

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Describe your experience designing a multi-agent system: how did you handle orchestration, error recovery, and inter-agent communication?
2 How would you implement a RAG pipeline for carbon footprint data? Walk through data ingestion, chunking, embedding, retrieval, and evaluation.
3 How do you balance using LLMs versus rule-based logic for reproducibility? Give an example of a trade-off you made.
4 What metrics do you use to evaluate the quality and accuracy of LLM outputs in production? How do you handle failures?
5 How would you design a scalable backend service for processing carbon footprint requests with varying data quality and volume?
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⚠️ Common Mistakes to Avoid

  • Don't focus solely on generic backend experience without emphasizing AI agents and LLMs—this role is specifically about agent-based systems.
  • Avoid vague claims about 'AI experience' without concrete examples of building tool-calling or RAG systems in production.
  • Don't neglect the climate tech aspect—failing to show genuine interest in the mission can be a red flag for a mission-driven startup.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Global Changer!

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