AI Software Engineer – Augmented Developer

Jobtailor

Paris

Sur place

EUR 70 000 - 100 000

Plein temps

14 jours+

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Résumé du poste

Jobtailor in Paris, France is seeking a backend engineer to design and develop ultra-high-performance backends using Python and FastAPI. You will master Anthropic Claude, implement advanced RAG architectures, and connect LLMs to company databases and APIs.

The role emphasizes maintainability, security and performance, and offers the opportunity to shape cutting-edge ML-powered products. Collaborate with data teams and product managers in a fast-paced environment.

Qualifications

  • 4+ years of experience in software engineering.
  • Advanced proficiency in Python and the FastAPI framework.
  • Experience with the Anthropic API and agent/framework ecosystem (LangChain / LlamaIndex / CrewAI).
  • DevOps / Cloud culture and experience.

Responsabilités

  • Design and develop ultra-high-performance backends using Python and FastAPI.
  • Master and leverage the full potential of Anthropic models (Claude).
  • Implement advanced RAG architectures and connect LLMs to company databases and APIs.
  • Ensure the maintainability, security, and performance of the code delivered.

Connaissances

Python
FastAPI
Anthropic API
LangChain
LlamaIndex
Cloud DevOps

Outils

AWS
Docker

Description du poste

Responsibilities


  • Design and develop ultra-high-performance backends using Python and FastAPI

  • Master and leverage the full potential of Anthropic models (Claude)

  • Implement advanced RAG architectures and connect LLMs to company databases and APIs

  • Ensure the maintainability, security, and performance of the code delivered


Requirements


  • At least 4 years of experience in software engineering

  • Advanced proficiency in Python and the FastAPI framework

  • Experience with the Anthropic API and the agent/framework ecosystem (LangChain, LlamaIndex, or CrewAI)

  • DevOps / Cloud culture and experience

  • Obsession with best practices: apply rigorous discipline to safely and effectively harness the power of LLMs

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