AI Backend Engineer - Python, AWS, LLMs (Production)

Emeria Technologies

Paris

On-site

EUR 50,000 - 80,000

Full time

14 days+
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Job summary

Jobtailor is seeking a backend engineer to design and develop the backend for AI systems. You will build AI-focused backend applications with Python, emphasizing production-grade code.

Contribute to the backend of Reemia’s interface, implementing architectures for agent orchestration capable of handling large data volumes, in a cloud environment (AWS). Focus on resilience, maintainability, testing, and security while delivering robust, scalable services and continuous improvements in production

Qualifications

  • 2+ years of backend engineering experience.
  • Strong command of Python.
  • Good understanding of cloud architectures, ideally AWS.
  • Strong engineering culture: code quality, testing, performance, security.
  • Bonus: practical experience with AI or LLM projects in production.

Responsibilities

  • Design and develop the backend for AI systems.
  • Build AI-focused backend applications and services, with extensive exposure to language models (LLMs).
  • Design robust architectures for agent orchestration, capable of handling large volumes of data.
  • Contribute to the backend of Reemia’s interface, a generative AI platform for real estate.
  • Develop solutions in Python with a production-first mindset: code quality, testability, and maintainability.
  • Industrialize and scale solutions for production.
  • Contribute to the resilience and availability of production systems.
  • Ensure maintenance, evolution, and bug fixes for systems in production.

Skills

Python
2+ years backend
AWS
code quality
LLM exposure

Tools

Python
AWS

Job description

Jobtailor is seeking a backend engineer to design and develop the backend for AI systems. You will build AI-focused backend applications with Python, emphasizing production-grade code.

Contribute to the backend of Reemia’s interface, implementing architectures for agent orchestration capable of handling large data volumes, in a cloud environment (AWS). Focus on resilience, maintainability, testing, and security while delivering robust, scalable services and continuous improvements in production

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