Founding Software Engineer

Global M
Madrid
EUR 50.000 - 70.000
Descripción del empleo

Responsibilities

  • Design and implement production-grade AI systems with focus on LLMs and autonomous agents
  • Develop optimized RAG systems and embedding pipelines
  • Build robust Python applications emphasizing async programming and performance
  • Deploy, monitor and maintain AI systems in production
  • Establish a scalable and maintainable technical foundation, balancing speed of delivery with long-term scalability
  • Champion the use of AI, staying at the forefront of Agentic AI advancements, and apply these learnings to deliver value-driven solutions.
  • Be part of building our performance-driven culture that values transparency, simplicity, and rapid iteration - you will be part of the basis of our culture
  • Foster a culture of learning and innovation, enabling the team to adapt to the rapidly evolving AI landscape.

Desired Skills

  • You have experience building in a fast-iteration environment - ideally with start-up experience
  • You solve problems quickly and enjoy delivering outcomes, not just fancy technology
  • You love learning and being part of a high-performing team
  • Data-first - you love thinking on a data-first mindset and its possibility to create better products, experiences and business models down the line
  • You’ve been deploying agent architectures / LLMs in production and at scale

Technical Skills

  • Testing, logging, monitoring, performance optimization
  • Knowledge of data storage solutions (both SQL and NoSQL)
  • Experience with high-throughput, low-latency systems

LLMs Usage

  • Strong understanding of LLMs, prompt engineering, chaining, caching and model fine-tuning
  • Experience with both open-source and closed-source LLMs
  • Experience with Retrieval-Augmented Generation (RAG), embedding optimization, chunking strategies, vector databases (ChromaDB, Pinecone)
  • Experience with LLM frameworks (Llamaindex / Langchain)
  • Model evaluation metrics and performance benchmarks
  • LLM guardrails and security best practices
  • LLM cost-optimisation strategies
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