Desarrollador/a Fullstack

Tendios is a SaaS platform for Spanish public procurement intelligence. Our products, Bid and Create, help companies find, analyse and win public tenders. Tender documentation is long, unstructured and full of legal and technical detail, which makes it an ideal ground for applied AI.

We are looking for a Senior AI/ML Engineer who designs the intelligence behind our products and ships it into production code. You will decide which model solves each problem, whether a commercial API, a fine-tuned open-source model or a combination, and make it fast, reliable and affordable at scale.

This is not a research-only position. You will prototype in Python, then integrate and ship features in our Node.js / NestJS / TypeScript platform alongside the rest of the engineering team. You will join our AI & Data squad.

AI architecture and models

Commercial LLMs, cost-efficient

Open-source models

Product engineering

• 5+ years of software engineering, including 2+ years building LLM or ML features that run in production.
• Strong Python for ML work: PyTorch, Hugging Face Transformers and the surrounding data tooling.
• Solid TypeScript and Node.js in production; NestJS experience or the ability to become productive in it quickly.
• Deep hands-on knowledge of commercial LLM APIs, including concrete examples of how you reduced their cost without losing quality.
• Practical experience fine-tuning, quantising and serving open-source models, and a clear view of when self-hosting pays off versus an API.
• RAG in production: embeddings, vector search (e.g. pgvector, Qdrant, OpenSearch), hybrid retrieval and reranking.
• An evaluation mindset: you build test sets, use LLM-as-judge carefully and measure before you claim improvements.
• Comfort with the full lifecycle: Docker, CI/CD, cloud infrastructure, observability and on-call ownership of what you ship.
• Fluent English for technical work.
• Spanish, since our source documents and many users work in Spanish.
• Agents and tool use: function calling, the Model Context Protocol (MCP) and multi-step workflows.
• Document AI: PDF parsing, OCR, layout and table extraction from messy files.
• LLM observability and gateway tools such as Langfuse, LiteLLM or similar.
• GPU infrastructure and cost management on AWS, GCP or specialised GPU providers.
• Knowledge of public procurement, legal or other document-heavy domains.
• Awareness of GDPR and the EU AI Act when designing AI features.

How to apply

Send your CV and a short note describing one LLM feature you took to production: the model choice, how you evaluated it, and what you did to bring its cost down. Links to code, papers or open-source work are welcome.