Backend Engineer | Python & Generative AI (RAG/LLMs)

INNOCV Solutions; Madrid; On-site; EUR 60,000 - 85,000; Full time

Benefits offered by this job

Flexible hours

Birthday day off

Gympass wellness

Volunteering opportunities

Company events

Job summary

INNOCV Solutions is seeking a Backend Engineer with strong Python expertise and hands-on experience building and operating Generative AI solutions in production. You will design, develop and maintain backend services powered by Large Language Models (LLMs), agentic workflows and Retrieval-Augmented Generation (RAG) architectures.

The role blends solid software engineering with advanced AI practices for reliability and observability.

Qualifications

• 5+ years of professional backend development experience with Python.
• 2+ years of hands-on experience delivering LLM-based solutions in production.
• Experience with Azure OpenAI and/or the OpenAI API.
• Practical experience with LangChain and LangGraph.
• Experience building production RAG systems using Azure AI Search or similar technologies.
• Strong knowledge of FastAPI, asynchronous programming and streaming.
• Experience with LLM evaluation and observability.
• Experience using AI coding tools such as Claude Code or Codex, and working with skills and MCP servers.
• Professional working proficiency in English.

Skills

Python

LLM development

Azure OpenAI/OpenAI API

LangChain

LangGraph

RAG systems

FastAPI

Asynchronous programming

LLM evaluation

Observability

AI coding tools (Claude Code/Codex)

English proficiency

Tools

Azure OpenAI

OpenAI API

LangChain

LangGraph

Azure AI Search

Job description

INNOCV Solutions is seeking a Backend Engineer with strong Python expertise and hands-on experience building and operating Generative AI solutions in production. You will design, develop and maintain backend services powered by Large Language Models (LLMs), agentic workflows and Retrieval-Augmented Generation (RAG) architectures.

The role blends solid software engineering with advanced AI practices for reliability and observability.