TL;DR

Machine Learning Engineer (AI): Designing, training, and deploying innovative, production-grade computer vision models and LLM-powered components for vehicle claims and ownership with an accent on scalable data and ML pipelines. Focus on owning the end-to-end ML lifecycle on GCP, including deployment, operation, and performance optimization.

Location: Virtual Spain

Company

%hirify_global% leverages AI and data assets to develop, deliver, and maintain components that simplify vehicle claims and ownership.

What you will do

Design, train, and ship computer vision models (classification, detection, segmentation) and LLM-powered components.
Build scalable data and ML pipelines on GCP (BigQuery, Dataflow, Vertex AI) for training and inference.
Deploy and operate services on GKE/Cloud Run using Docker and Kubernetes, following CI/CD practices.
Expose models via FastAPI, build internal tools with Streamlit, and instrument monitoring with Grafana.
Own the end-to-end ML lifecycle: problem framing, data curation, experimentation, productization, optimization, and monitoring.
Contribute to a high-quality monorepo through code reviews, standards, and documentation.

Requirements

Strong Python and software engineering fundamentals (testing, code quality, CI/CD, performance).
Proven experience training and deploying computer vision models with TensorFlow/PyTorch.
Proficiency with large-scale datasets and distributed processing on GCP or similar platforms.
Production MLOps experience on Kubernetes/containers.
Ability to design clean APIs (FastAPI) and build usable internal tools (Streamlit).
Experience with tree-based models and integrating LLM APIs into production workflows.
Effective communication and collaboration in a distributed environment.

Nice to have

Vertex AI pipelines experience.
GPU optimization and cost/performance tuning for training/inference.
Experience in insurance, automotive, or related computer vision domains.

Culture & Benefits

Work in a monorepo with strong build, CI/CD, and code quality practices.
High autonomy and ownership with freedom to choose the best tools for the job.
Focus on a production mindset: reliability, observability, maintainability, and measurable impact.
Collaborate within an internationally distributed team, sharing best practices and improving workflows.