At Mindsight Ventures, we’re building next-generation AI-powered platforms that connect data, people, and intelligent systems in fields like health, finance, and automation .
We partner with startups, enterprises and venture builders to design intelligent systems — from AI agents and data platforms to full-stack products — helping companies move faster from idea to execution.
Our team works across multiple domains — product design, software development, AI research, and cloud infrastructure — always guided by the same principles: clarity, efficiency, and real-world impact .
We’re based in Barcelona, with a hybrid work model and an international team of engineers, designers, and entrepreneurs.
We’re looking for a talented AI Engineer to help us design, build, and deploy AI-driven systems and backend infrastructures for our internal platforms and client projects.
This role bridges software engineering, cloud DevOps, and machine learning deployment — ideal for someone who enjoys solving complex technical challenges and building scalable systems.
Develop and maintain backend services (Node.js, TypeScript, or Python)
Design APIs, data pipelines, and system architectures
Implement and deploy AI/ML models (training, testing, inference, and MLOps)
Work with cloud environments (Azure, AWS, or GCP) for deployment and scaling
RAG, vector databases, model serving)
Collaborate with the front-end and AI research teams
2–4 years of experience in backend or AI development
~ Strong programming skills in TypeScript and/or Python
~ Experience with cloud infrastructure (Azure preferred) and CI/CD
~ Knowledge of databases (SQL/NoSQL), APIs, and containers (Docker)
~ Understanding of ML frameworks and deployment workflows
~ Experience training or fine-tuning computer vision models (e.g., Knowledge of vector embeddings and multimodal architectures combining text and image data
Experience with AI inference optimization (GPU deployment, quantization, ONNX, TensorRT, etc.)
Understanding of data versioning and MLOps pipelines for model reproducibility