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Ai engineer / backend developer (oña)

Oña
Mindsight Ventures
Publicada el 9 noviembre
Descripción

About Mindsight Ventures
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.
About the role
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
— idóneo for someone who enjoys solving complex technical challenges and building scalable systems.
Your responsibilities
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
Explore modern AI infrastructure patterns (e.g.,
RAG
, vector databases, model serving)
Collaborate with the front-end and AI research teams
Requirements
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
Strong architectural thinking and curiosity for new technologies
Nice to have
Experience training or fine-tuning
computer vision models
(e.g., CNNs, Vision Transformers, etc.)
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
Interest in
AI agents
,
RAG pipelines
, and integrating vision models into production environments

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