Desarrollador/a Fullstack (Barcelona)

Desarrollador/a Fullstack (Barcelona)

09 oct
|
Tendios
|
Barcelona

09 oct

Tendios

Barcelona

About Tendios
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 adecuado ground for applied AI.
The role
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.
What you'll do
AI architecture and models
• Design the AI systems behind our features: extraction, classification, matching, summarisation, drafting and agentic workflows over tender documentation.

• Choose the right approach per use case: prompting, RAG,



fine-tuning, distillation or classic ML, and justify it on quality, latency and cost.

• Build retrieval pipelines: chunking, embeddings, hybrid search and reranking over large document collections.

Commercial LLMs, cost-efficient
• Own our usage of commercial providers (Anthropic, OpenAI, Google and others) and keep cost per feature under control.

• Apply prompt caching, batch APIs, model routing and cascading, semantic caching, context compression and token budgets.

• Track cost, latency and quality per feature and per customer, and set alerts before bills surprise us.

Open-source models
• Evaluate, fine-tune (LoRA / QLoRA) and deploy open-weight models such as Llama, Mistral, Qwen or Gemma where they beat APIs on cost, privacy or control.

• Serve them efficiently with tools like vLLM, TGI or llama.cpp, using quantisation and batching to get the most from each GPU.

• Build the data pip

📌 Desarrollador/a Fullstack (Barcelona)
🏢 Tendios
📍 Barcelona

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