AI Engineer

About The Role

We are looking for an AI Engineer who thrives at the intersection of rapid experimentation and agile product development. In this role, you will be the bridge between the latest AI developments and tangible product impact. You aren't just following a roadmap; you are helping define it by proving what is possible with the latest frontier models and architectures. You will be responsible for the "quality loop": moving from a promising proof-of-concept to a highly reliable, optimized, and validated product.

What You’ll Be Doing

• Product-Centric Development: Designing and executing experiments to improve GenAI capabilities. This isn't just about "accuracy" in a vacuum—it's about optimizing for user value, reliability, and cost-effectiveness.
• Evaluation Systems: Building the "Golden Path" for quality. You will design and implement robust, multi-dimensional evaluation suites (e.g., using "LLM-as-a-judge," semantic checks, and unit tests) to ensure our features are production-ready and hallucination-resistant.
• Advanced RAG & Reasoning Optimization: Moving beyond "naive RAG." You will implement and tune advanced retrieval strategies (e.g., hybrid search, reranking, agentic retrieval) and optimize complex reasoning loops (e.g., CoT, ReAct) to make our current and future agents smarter and more reliable.
• Production-Grade Model Tuning: Leading the strategy for when, and if, to move beyond simple prompting. You will oversee supervised fine-tuning (SFT) and Parameter-Efficient Fine-Tuning (LoRA) workflows to adapt models to our specific product domains.
• Performance & Cost Engineering: Balancing the "Quality-Cost-Latency" triangle. You will find ways to maintain high-quality outputs while optimizing token usage and reducing inference latency.

Location

The role is based in Spain

Way of Working: Remote / Hybrid

What You’ll Need

• Applied MLE Background: You have a proven track record of shipping Machine Learning functionality in a product-focused environment. You prefer "what works in practice" over "what works in theory."
• Bleeding-Edge Awareness: You are a "first adopter" of new AI technologies. You are intimately familiar with the trade-offs of different model architectures, prompting techniques, and fine-tuning methods.